>> live from San Francisco, California. It's the key. You covering the IBM chief Data officer? Someone brought to you by IBM. >> We're back at the IBM CDO conference. Fisherman's Worf Worf in San Francisco. You're watching the Cube, the leader in life tech coverage. My name is David Dante. Glenn Finches. Here's the global leader of Big Data Analytics and IBM, and we're pleased to have Mark Clare. He's the head of data enablement at AstraZeneca. Gentlemen, welcome to the Cube. Thanks for coming on my mark. I'm gonna start with this head of data Data Enablement. That's a title that I've never heard before. And I've heard many thousands of titles in the Cube. What is that all about? >> Well, I think it's the credit goes to some of the executives at AstraZeneca when they recruited me. I've been a cheap date officer. Several the major financial institutions, both in the U. S. And in Europe. Um, AstraZeneca wanted to focus on how we actually enable our business is our science areas in our business is so it's not unlike a traditional CDO role, but we focus a lot more on what the enabling functions or processes would be >> So it sounds like driving business value is really the me and then throw. Sorry. >> I've always looked at this role in three functions value, risk and cost. So I think that in any CDO role, you have to look at all three. I think the you'd slide it if you didn't. This one with the title. Obviously, we're looking at quite a bit at the value we will drive across the the firm on how to leverage our date in a different way. >> I love that because you can quantify all three. All right, Glenn. So you're the host of this event. So awesome. I love that little presentation that you gave. So for those you didn't see it, you gave us pay stubs and then you gave us a website and said, Take a picture of the paste up, uploaded, and then you showed how you're working with your clients. Toe. Actually digitize that and compress all kinds of things. Time to mortgage origination. Time to decision. So explain that a little bit. And what's that? What's the tech behind that? And how are people using it? You know, >> for three decades, we've had this OCR technology where you take a piece of paper, you tell the machine what's on the paper. What longitudinal Enter the coordinates are and you feed it into the hope and pray to God that it isn't in there wrong. The form didn't change anything like that. That's what that's way. We've lived for three decades with cognitive and a I, but I read things like the human eye reads things. And so you put the page in and the machine comes back and says, Hey, is this invoice number? Hey, is this so security number? That's how you train it as compared to saying, Here's what it So we use this cognitive digitization capability to grab data that's locked in documents, and then you bring it back to the process so that you can digitally re imagine the process. Now there's been a lot of use of robotics and things like that. I'm kind of taken existing processes, and I'm making them incrementally. Better write This says look, you now have the data of the process. You can re imagine it. However, in fact, the CEO of our client ADP said, Look, I want you to make me a Netflix, not a blood Urbach Blockbuster, right? So So it's a mind shift right to say we'll use this data will read it with a I will digitally re imagine the process. And it usually cuts like 70 or 80% of the cycle time, 50 to 75% of the cost. I mean, it's it's pretty groundbreaking when you see it. >> So markets ahead of data neighborhood. You hear something like that and you're not. You're not myopically focused on one little use case. You're taking a big picture of you doing strategies and trying to develop a broader business cases for the organization. But when you see an example like that and many examples out there, I'm sure the light bulbs go off. So >> I wrote probably 10 years cases down while >> Glenn was talking about you. You do get tactical, Okay, but but But where do you start when you're trying to solve these problems? >> Well, I look att, Glenn's example, And about five and 1/2 years ago, Glenn was one I went to had gone to a global financial service, firms on obviously having scale across dozens of countries, and I had one simple request. Thio Glenn's team as well as a number of other technology companies. I want cognitive intelligence for on data in Just because the process is we've had done for 20 years just wouldn't scale not not its speed across many different languages and cultures. And I now look five and 1/2 years later, and we have beginning of, I would say technology opportunities. When I asked Glenn that question, he was probably the only one that didn't think I had horns coming out of my head, that I was crazy. I mean, some of the leading technology firms thought I was crazy asking for cognitive data management capabilities, and we are five and 1/2 years later and we're seeing a I applied not just on the front end of analytics, but back in the back end of the data management processes themselves started automate. So So I look, you know, there's a concept now coming out day tops on date offices. You think of what Dev Ops is. It's bringing within our data management processes. It's bringing cognitive capabilities to every process step, And what level of automation can we do? Because the, you know, for typical data science experiment 80 to 90% of that work Estate engineering. If I can automate that, then through a date office process, then I could get to incite much faster, but not in scale it and scale a lot more opportunities and have to manually do it. So I I look at presentations and I think, you know, in every aspect of our business, where we clear could we apply >> what you talk about date engineering? You talk about data scientist spending his or her time just cleaning the wrangling data, All the all the not fun stuff exactly plugging in cables back in the infrastructure date. >> You're seeing horror stories right now. I heard from a major academic institution. A client came to them and their data scientists. They had spent several years building. We're spending 99% of their time trying to cleanse and prep data. They were spend 90% cleansing and prepping, and of the remaining 10% 90% of that fixing it where they fix it wrong and the first time so they had 1% of their job doing their job. So this is a huge opportunity. You can start automating more of that and actually refocusing data science on data >> science. So you've been a chief data officer number of financial institutions. You've got this kind of cool title now, which touches on some of the things a CDO might do and your technical. We got a technical background. So when you look a lot of the what Ginny Rometty calls incumbents, call them incumbent Disruptors two years ago at Ivy and think they've got data that has been hardened, you know, in all these projects and use cases and it's locked and people talk about the silos, part of your role is to figure out Okay, how do we get that data out? Leverage. It put it at the core. Is that is that fair? >> Well, and I'm gonna stay away from the word core cause to make core Kenan for kind of legacy processes of building a single repositories single warehouse, which is very time consuming. So I think I can I leave it where it is, but find a wayto to unify it. >> Not physically, exactly what I say. Corny, but actually the court, that's what we need >> to think about is how to do this logically and cream or of Ah unification approach that has speed and agility with it versus the old physical approaches, which took time. And resource is >> so That's a that's a computer science problem that people have been trying to solve for years. Decentralized, distributed, dark detectors, right? And why is it that we're now able Thio Tap your I think it's >> a perfect storm of a I of Cloud, the cloud native of Io ti, because when you think of I o. T, it's a I ot to be successful fabric that can connect millions of devices or millions of sensors. So you'd be paired those three with the investment big data brought in the last seven or eight years and big data to me. Initially, when I started talking to companies in the Valley 10 years ago, the early days of, um, apparatus, what I saw or companies and I could get almost any of the digital companies in the valley they were not. They were using technology to be more agile. They were finding agile data science. Before we call the data signs the map produce and Hadoop, we're just and after almost not an afterthought. But it was just a mechanism to facilitate agility and speed. And so if you look at how we built out all the way up today and all the convergence of all these new technologies, it's a perfect storm to actually innovate differently. >> Well, what was profound about my producing in the dupe? It was like leave the data where it is and shipped five megabytes a code two upended by the data and that you bring up a good point. We've now, we spent 10 years leveraging that at a much lower cost. And you've got the cloud now for scale. And now machine intelligence comes in that you can apply in the data causes. Bob Pityana once told me, Data's plentiful insights aren't Amen to that. So Okay, so this is really interesting discussion. You guys have known each other for a couple of couple of decades. How do you work together toe to solve problems Where what is that conversation like, Do >> you want to start that? >> So, um, first of all, we've never worked together on solving small problems, not commodity problems. We would usually tackle something that someone would say would not be possible. So normally Mark is a change agent wherever he goes. And so he usually goes to a place that wants to fix something or change something in an abnormally short amount of time for an abnormally small amount of money. Right? So what's strange is that we always find that space together. Mark is very judicious about using us as a service is firm toe help accelerate those things. But then also, we build in a plan to transition us away in transition, in him into full ownership. Right. But we usually work together to jump start one of these wicked, hard, wicked, cool things that nobody else >> was. People hate you. At first. They love you. I would end the one >> institution and on I said, OK, we're going to a four step plan. I'm gonna bring the consultants in day one while we find Thailand internally and recruit talent External. That's kind of phases one and two in parallel. And then we're gonna train our talent as we find them, and and Glenn's team will knowledge transfer, and by face for where, Rayna. And you know, that's a model I've done successfully in several organizations. People can. I hated it first because they're not doing it themselves, but they may not have the experience and the skills, and I think as soon as you show your staff you're willing to invest in them and give them the time and exposure. The conversation changes, but it's always a little awkward. At first, I've run heavy attrition, and some organizations at first build the organizations. But the one instance that Glen was referring to, we came in there and they had a 4 1 1 2 1 12 to 15 year plan and the C I O. Looked at me, he says. I'll give you two years. I'm a bad negotiator. I got three years out of it and I got a business case approved by the CEO a week later. It was a significant size business case in five minutes. I didn't have to go back a second or third time, but we said We're gonna do it in three years. Here's how we're gonna scale an organization. We scaled more than 1000 person organization in three years of talent, but we did it in a planned way and in that particular organization, probably a year and 1/2 in, I had a global map of every data and analytics role I need and I could tell you were in the US they set and with what competitors earning what industry and where in India they set and in what industry And when we needed them. We went out and recruited, but it's time to build that. But you know, in any really period, I've worked because I've done this 20 plus years. The talent changes. The location changes someone, but it's always been a challenge to find him. >> I guess it's good to have a deadline. I guess you did not take the chief data officer role in your current position. Explain that. What's what. What's your point of view on on that role and how it's evolved and how it's maybe being used in ways that don't I >> mean, I think that a CDO, um on during the early days, there wasn't a definition of a matter of fact. Every time I get a recruiter, call me all. We have a great CDO row for first time I first thing I asked him, How would you define what you mean by CDO? Because I've never seen it defined the same way into cos it's just that way But I think that the CDO, regardless of institutions, responsibility end in to make sure there's an Indian framework from strategy execution, including all of the governance and compliance components, and that you have ownership of each piece in the organization. CDO most companies doesn't own all of that, but I think they have a responsibility and too many organizations that hasn't occurred. So you always find gaps and each organization somewhere between risk costs and value, in terms of how how they're, how the how the organization's driving data and in my current role. Like I said, I wanted to focus. We want the focus to really be on how we're enabling, and I may be enabling from a risk and compliance standpoint, Justus greatly as I'm enabling a gross perspective on the business or or cost management and cost reductions. We have been successful in several programs for self funding data programs for multi gears. By finding and costs, I've gone in tow several organizations that it had a decade of merger after merger and Data's afterthought in almost any merger. I mean, there's a Data Silas section session tomorrow. It'd be interesting to sit through that because I've found that data data is the afterthought in a lot of mergers. But yet I knew of one large health care company. They've made data core to all of their acquisitions, and they was one the first places they consolidated. And they grew faster by acquisition than any of their competitors. So I think there's a There's a way to do it correctly. But in most companies you go in, you'll find all kinds of legacy silos on duplication, and those are opportunities to, uh, to find really reduce costs and self fund. All the improvements, all the strategic programs you wanted, >> a number inferring from the Indian in the data roll overlaps or maybe better than gaps and data is that thread between cost risk. And it is >> it is. And I've been lucky in my career. I've report toe CEOs. I reported to see Yellows, and I've reported to CEO, so I've I've kind of reported in three different ways, and each of those executives really looked at it a little bit differently. Value obviously is in a CEO's office, you know, compliance. Maurizio owes office and costs was more in the c i o domain, but you know, we had to build a program looking >> at all three. >> You know, I think this topic, though, that we were just talking about how these rules are evolving. I think it's it's natural, because were about 5 2.0. to 7 years into the evolution of the CDO, it might be time for a CDO Um, and you see Maur CEOs moving away from pure policy and compliance Tomb or value enablement. It's a really hard change, and that's why you're starting to Seymour turnover of some of the studios because people who are really good CEOs at policy and risk and things like that might not be the best enablers, right? So I think it's pretty natural evolution. >> Great discussion, guys. We've got to leave it there, They say. Data is the new oil date is more valuable than oil because you could use data to reduce costs to reduce risk. The same data right toe to drive revenue, and you can't put a gallon of oil in your car and a quart of oil in the car quarter in your house of data. We think it's even more valuable. Gentlemen, thank you so much for coming on the cues. Thanks so much. Lot of fun. Thanks. Keep right, everybody. We'll be back with our next guest. You're watching the Cube from IBM CDO 2019 right back.

>> Narrator: From Chicago, it's theCUBE. Covering Veritas Vision Solution Day, 2018. Brought to you by Veritas. >> Welcome back to Chicago everybody, you're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante, we're here covering the Veritas Vision Solution Day. Veritas last year had a big tent event thay thousands and thousands of customers. They decided this year to go out to the customers. Like us, we go out to the events, we extract the signal from the noise. Jeff Kroth is here, he's the manager of data management and analytics at Softchoice, which is a Veritas partner. Welcome to theCube, thanks for coming on, Jeff. >> Thanks for having me. >> So tell me more about Softchoice, what's your sort of niche and differentiation in the market? >> Sure, so Softchoice is about a two billion dollar North American IT Solution proivder, we're actually the number three Global Midmarket Managed Service provider. We provide the breadth and coverage across a variety of vendors, helping our customers modernize their IT infrastructure. >> So Midmarket is unique, you know, it's not big enough to have like thousands of people do it, data protection for example, they're Generalists, typically, IT Generalists, they're not small, not like the CEO doing the back up. So talk a little bit about the unique aspects of Midmarket from your perspective. >> Well I think some of the things that we bring to the bare Midmarker is helping customers who don't have that deep IT staff with our technology mentorship, with our skills transfer that we provide our customers, we have a managed service that we provide which really helps our customers do more with what they have. >> So data protection is one of the hottest topics going here at VMworld in August, and for the last two years it's been probably one of the hottest topics. That along with Cloud and obviously the AWS partnership with VMWare. Why is data protection so hot right now? What are the factors? >> I would say data protection and data management is hot. It actually comes back to the underlying data behind it, they say, Gardener says data is the new gold and the new natural resource. Well if you don't have your data protected, available, and modernized, you can't leverage things like data analytics to get the most out of your data. Our customers, we see, customers use data as a competitive advantage. Go back look at Blockbuster and Netflix, they weren't able to take advantage of their data and understand that, so really to me data protection is the foundation and building block to grow into an analytics environment where you're really taking advantage of the underlying data for that competitive advantage. >> And I want to do a little tangent here, cause when you hear things like, "data is the new oil, its the new gold," it's actually, in our view, even more valuable, and here's why. Oil, you can put a quart of oil in your car or in your house, but you can't put the same quart in both. Data, using the Netflix example, you can use the same data in a variety of different ways. So in some regards, it's even more valuable. So I guess the bottom line here is digital transformation, which is real, is all about how you use data and that has direct implications on how you protect data, doesn't it? >> It does. >> And so, the other thing is Cloud. You hear a lot of talk about Cloud, and Multicloud, and we're moving into this world of more distributed data. What kind of challenges does that present for customers? >> I mean we are a big Microsoft partner and have a big partnership with Azure, you know, helping our customers on that Cloud journey I think is an important part. One of the things and one of the trends that we're finding is ensuring that you're monerizing your current data platform as you do that data migration to the Cloud. One of the things we see is customers really struggle with cost containment as they make that Cloud migration. So being able to understand what the data is and ensuring that you're only moving the right amount of data and the right workloads to the Cloud to keep costs down, I think is one of the important things, one of the things we're helping our customers, making sure they're getting real value out of the Cloud and doing that cost containment. >> We heard this morning Joe T was talking about some Cloud repatriation and you definitely are seeing it he gave an example of a large company in Dubai who said, "we're going all in on Cloud," and they went all in on Cloud and said, "wow, this is really expensive." Make sense, right? Renting is often times more expensive than owning. So I look at that as, you know, those that have had to repatriate, a lot of that is poor planning so how do you help your customers plan which work loads should be in the Cloud and follow those laws of economics, and physics, and governance, you know the law of the land, how do you help them? >> So it's really a couple of things, we have a couple of assessments that we use to help customers understand their existing workloads and what makes sense to move to the Cloud and what makes sense to keep on premise. So that's an assessment that Softchoice offers. The other thing is aligning to Veritas's 360 data management strategy is really getting a deeper understanding of what that data is that you have so you're aligning the right costs associated with that data to decide what you move to the Cloud and what stays on prem and I think that's a big thing, it's really understanding what that data is and aligning it to what needs to be moved. >> We talked to senior leaders in IT and business, they tell us that if you got to move to the Cloud you really want to change the operating model, that's where you're going to get the biggest bang for the buck. What does that mean in terms of data protection? If you're going to go digital, go Cloud, change your operating model, that's going to have implications on data protection, isn't it? And what do you see as the-- >> It is, and what I think we're seeing in Softchoice as a whole, you know we are a big proponent of the Cloud, what I think we see that, you really don't think that customers are going to go fully Cloud. It's really taking that hybrid approach and aligning what applications make sense to go to the Cloud, what applications make sense to stay on prem. So really having that full view of your environment so you can make intelligent decisions on what to move to the Cloud and what to keep on prem, aligning to the usage of that data. >> Now what about your partnership with Veritas? You kind of exclusive Veritas, you work with other back up vendors? Maybe talk about that a little bit and then what do you see as Veritas's strengths and what's on their to-do list? >> Yeah, so we're a Veritas Gold Partner both in the US and in Canada. We're not an exclusive to Veritas, we like to take a very agnostic approach and really help customers understand what their environment looks like and what makes sense for them. Veritas is a key player as part of our data management strategy and going down the road of our analytics strategy, helping customers really understand the value of their data. You can't get into the analytics world unless your data is in the right place so, again we like to take an agnostic approach but Veritas does align very well from a data management strategy for Softchoice. >> Why, why is that? Is that their stack, they've just been around longer, they focus a lot on governance, and I heard things like categorization, throwing out Federal rules of civil procedure today, that's a long history, so why, what's so special? >> I would say it's the overall breadth of their portfolio, it's helping customers back up to Cloud, back up for the Cloud, it's helping customers do things like DR and replication. It's really getting that full 360 view, you know one of the things we're big on is things like Infomap and Data Insights and really helping customers really understand what the underlying data is, associating the cost with that, so as they move workloads to the Cloud they get a full understanding of what they're moving so they're just not blindly moving things to the Cloud, helping keep costs down. Again, when customers, like as in the example we saw earlier today, a lot of customers think that Cloud is a logical strategy for them but over time they see that it increases cost. So it's really about aligning the right sizing of your environment, moving the right applications, the right data to the Cloud and using that as part of your overall strategy. We really see customers really taking a hybrid approach, it's not ever going to be fully public Cloud, it's not going to be fully private Cloud, it's going to be a combination. >> So we're going to ask you about the competitive landscape cause you are sort of Switzerland here, even though got an affinity, it seems, to Veritas, but you've seen a lot of VC money move into the space, you're seeing a lot of specialists emerge, you've seen some startups come after the Incumbents like Veritas, certainly you know Commvault's another, IBM's another, of course DELL EMC, add those guys up they probably have three quarters on the market place so of course the startups are going to come after them. And they're got shiny new toys and probably developing in Cloud Native and probably talking all the right language. But how do you squint through the hype from the marketing side and sort of help customers figure out how they're going to have the greatest business impact? >> I mean I think that's a good point. I think we're seeing a lot of small niche players that are born in the Cloud or have this shiny new marketing collatoral that they're going to market with and I think what's important for us is making sure our customers understand a full road map on what they're trying to do. So, we do see a lot of upstarts that are going after some of the Veritas, the IBM, the DELL EMC businesses, the world. But it's really making sure you're not taking a point solution and trying to go forward with that, it's understanding Portfolio, like Veritas's that has that depth and breadth and really has that history and background. You know, Veritas has been doing this forever and they really know their stuff. >> Yeah, so we've stressed that platforms are important to pay attention to, you know an API based platform is going to beat a product every time and have some legs. It might be it might have other implications in terms of complexities, but it can drive your business forward as opposed to your point, being a point product. And I'm curious as to your thoughts, particularly as it relates to analytics, which is in your title. For years people have looked at back up as just insurance, people that are trying to get more out of it. But how are people using the corpus of back up data and analytics use cases, why the affinity between data protection and analytics? >> I think data protection and data management are kind of clumped into one category. If you don't have a modernized IT infrastructure and you don't have a good data management strategy, it's impossible, you know poor data in, poor data out. You can't make intelligent analytics decisions or have that data for your analytics team if the information isn't there and accessible and good data. So it's really having a very keen data management strategy enabling your analytics users to have the right data to make the right decisions, cause if you don't have the right data you can't make the right decisions, and no analytics tool can go in and make informed decisions based off bad data. So data management is definitely part of the overall analytic strategy cause it's really the first step. >> And why the, in the back up corpuses, because you've got visibility on that data and it's the logical-- >> Sure. >> The logical one place, even if it's virtual, to actually be able to do those analytics, right? >> Exactly. >> Okay, and then I'll give you the last word. Thing's that your learning here today at the Vision Event, customers obviously Chicago, big customer center, you're based in Atlanta another big customer center. We were just in New York a few weeks ago meeting some pretty senior level folks. What are you learning here, what's the conversation like? >> I think the one key thing that I've taken out is that really customers aren't going full Cloud. It's you know, I think I saw a stat and 92% of customers are taking a hybrid approach and leveraging a really full data management policy to be able to handle on prem, to be able to handle private Cloud, public Cloud, and the combination. Really having that tool set to give you visualizations across an entire hybrid IT infrastructure I think it important. And that's really one of the key takeaways. >> We would agree, we've talked for quite some time now, years actually how organizations can't just shove data into the Cloud, they can't just put their business up into the public Cloud, rather they need to move the Cloud operating model to their business. it's very clearly, that's the trend, you're seeing so many signs of that. AWS and VMware partnering up. You certainly saw Google do that and this summer with Istio on prem, Microsoft obviously with Azure Stack, huge presence in hybrid Cloud. So those predictions are coming true. Jeff thanks very much for coming to theCUBE, great to see you. >> Yep, thanks for having me. >> Oh you're very welcome. Alright, keep it right there everybody, this is Dave Vellante, we'll be back from Veritas Vision Day in Chicago at the Palmer House Hotel, you're watching theCube. (soft techno music)

>> From Tavern on the Green, in Central Park, New York, it's theCUBE, covering Veritas Vision Solution Day, brought to you by Veritas. >> Welcome back to the beautiful Tavern on the Green, in the heart of Central Park. You're watching theCUBE, the leader in live tech coverage. My name's Dave Vellante. We're covering Vertias Solution Days, #VtasVision. Veritas used to have the big, single tent, big tent customer event, and decided this year, it's going to go belly to belly. Go out to 20 cities, intimate customer events where they can really sit down with customers across from the table; certainly, this beautiful venue is the perfect place to do that. Harish Venkat is here as the VP of Marketing and Global Sales Enablement at Veritas. Thanks for coming on, Harish. >> Yeah, thanks for having me. >> So, we're going to change it up a little bit. Let's hit the Escape key a few times and talk about >> Yeah. >> some of the big mega trends that you're seeing. You spend a lot of time with customers. You had some intimate conversations today. What do you see as the big trends driving the marketplace? >> So at my level, what I observe with the highest thing is simplicity, instant gratification, is two things that customers love. Forget about customers, even we as individuals, we love simplicity and instant gratification. Examples around that, you know, think about back in the days where you had to take a picture, process the film, and then realize, "oh my god, the film's not even worth watching." Now you have digital photography, you take millions of pictures, and instantly you view the picture, and keep whatever you want, delete whatever you don't want. A small example of how simplicity and instant gratification is changing the world. In fact, if you listen to Warren Buffett, he'll say, "Invest in companies that is making your life a lot easier," so, if I spread that across the entire industry, I can go on with examples like Netflix disrupting Blockbuster because it made it easy for customers to watch movies at their time, and making it easy for consumption. You look at showrooming concept, where you go to Best Buy's of the world and many others, and look at a product, but you don't buy it right there. You go to your phone and say, "okay, do I do a price compare?" And then order it on the phone, where someone delivers it to your house So the list goes on and on, and the underpinning result as a result of this is disruption, all right? You look at Fortune 500 companies, just in the last decade. Over 52% of those companies have been disrupted and the underpinning phenomenon is all about instant gratification and simplicity. >> And Amazon is another great example of, I remember when my wife said to me, "Dave, you got to invest in this company." It was like... 1997. >> Yeah. >> Invest in this company, Amazon? >> Yeah. >> At the time, it was mostly books, but they started to get into other retail, so right-- >> We missed that boat, didn't we? >> I actually did, but I sold, ah! (laughs) >> I never lost money making a profit, so okay. So, at the same time, customer... Customers just can't get there... >> Yeah. >> Overnight, so what are some of the challenges that they have in getting to that level of simplicity? >> Yeah, so you look at IT spend, and when you look at the breakdown of IT spend, you'll see that about 87%, and in many cases, even greater than 90%, they spend just to keep the lights on and these are well-established companies that I'm talking about. In fact, I was doing a Keynote in, in Minneapolis one time and a CIO came and said, "Harish, I totally disagree." "In my company, it's 96%." >> (Dave laughs) >> Just to keep the lights on! So you're talking about less than 10% of your IT spend gone towards innovation, and then you look at emerging companies who are spending almost 100% all around innovation, leveraging the clouds of the world, leveraging the latest and greatest technology, and then doing these disruptions, and making things simple for consumption, and as a result, the disruption happens, so I think we have an opportunity to re-balance the equation in the enterprise space, and making it more available for innovation than just keeping the lights on. >> So part of that... the equation of shifting that needle, moving that needle, if you will, just eliminating non-value-producing activities that are expensive. We know, still, IT is still very labor-intensive, so we got to take that equation down and shift it. Are you seeing companies have success in shifting, re-training people toward digital initiatives and removing some of the heavy lifting, and what's driving that? >> Yeah, so I think it's a journey, right? So, I mean, the entire notion of journeying to the cloud is one of the big initiative to take out heavily manual-intensive, data center-intensive, which is costing a lot of money. If I can just shift all of those workloads to the cloud, that'll help me re-balance the equation. I view the concept of data intensity, which is really two variables to it. Back to your point, if I can take the non-core activity, rely on my partner ecosystem to say what is best in class solutions that I can use as my foundation layer, and then innovate on top of it, then yes, you have the perfect winning formula to really have a lot of market share and wallet share. If you're trying to do the entire stack by yourself, good luck. You'll be one of those guys who will be disrupted. There is no doubt. >> So well, okay, that says partnerships are very important. >> Without a doubt. >> You're not too alone. >> Channel is very important. >> Yes. >> So, so what do you see, in terms of the ebb and flow in the industry, of partnerships, how those are forming? Hear a lot about "co-opetition," which is kind of an interesting term, that is now, we're living. >> Yeah. >> What's your, what's your observation about partnerships, and how companies are able to leverage them? What's best practice there? >> Yeah, so just as Veritas, we're a data protection leader company. We have incredible market share and wallet share, amongst the Fortune 500 and Fortune 100 companies, but even within our incredible standing, we have to rely on other partners. We don't do everything on our own. We have incredible relationship with our cloud service providers, with the hyper-converged system to the world, like Nutanix. We just announced Pure today, so when we combine those partnerships, we can offer incredible solutions for our customers, who can then take care of the first variable that I talked about, and then innovate on top of it. So I think partner ecosystem is extremely important. For customers, it's very important that they pick the right players, so they don't have to worry about the data, and they can continually focus on innovation. >> We were talking to NBC Universal today, and one of themes in my take-aways was he's trying to get to the... he's a, basically a data protector, backup administrator, essentially, but he's trying to get to the point where he can get the business lines to self-serve. >> Yeah. >> And that seems to me to be part of the simplicity. Now... an individual like that, got to re-skill. Move toward a digital transformation. Move that needle so it's not 90% keeping the lights on. It's maybe you get to 50/50. >> Yeah. What are you seeing in terms of training and re-education of both existing people and maybe even how young people are being educated, your thoughts? >> Yeah, I think the young people coming out of college, they're already tuned to this, so to me, those are the disruptors of the world. You got to keep an eye on those millennials of the world because you don't have to train them more, because they're coming out of college, you know. They don't have the legacy background. They don't have the data centers of the world. They are already in the cloud. They're born in the cloud, sort of individuals, so I think the challenge is more about existing individuals who have the pedigree of all the journey that, you and I, we have seen, and how do you re-tune yourself to the modern world? And I think that presents an opportunity to say, "Okay look, if you don't adapt real quick," "you don't have a chance to survive" "in this limited amount of time you have in the IT space," but having said that, we're also seeing that you have some time window, and that time window will continue to shrink, so when we talk about this transformation journey, you can see year after year, the progress that, that's been made in the transformation, this leap and bound, and that's all related to Moore's Law. You think about computer and storage, it's becoming a lot cheaper, and so the innovation rate is continuing to go up. So you have very limited window: adapt or die. >> So, Harish, we were talking about, we've talked about digital transformation. We talk about simplifying; we're talking about agility. We're talking about shifting budget priorities, all very important initiatives. How is Veritas helping customers achieve these goals, so that they can move the needle from 90% keep the lights on to maybe 50/50, and put more into innovation. >> So four major themes: one is data protection. If you don't have your core enterprise asset, which is your data protected, then you can't really innovate anything on top of it. You'll constantly be worrying about what happens if I have a ransomware attack, what if I have a data outage, so Veritas takes care of it, back to the notion that you pick the best players to take care of the fundamental layer, which is around the data. The second thing that I... I would say Veritas can help is the journey to the cloud. Cloud, again, is another instrument for you to take out cost out of your data center. You're agile, you're nimble, so you can focus on innovation. Do you see the trend? So again, Veritas helps you with that journey to the cloud. It allows to move data and application to the cloud. When you're in the cloud, we protect your data in the cloud. The third thing I would say is doing more with less. I talked about the IT equation already. Software-defined storage allows you to do that. And the last thing I would say is compliance. We can't get away from compliance, the fact that Veritas has solutions to have visibility around the data. You can classify the data. You can always be compliant working with Veritas. You take care of these four layers, you don't have to worry about your data asset. You can worry about innovation at that point. >> So it, to me, it's sort of a modern version of the rebirth of Veritas. When Veritas first started, I always used to think of it as a data management company, not just a backup company. >> Right. >> And that's really what we're talking about here today, evolving toward a data-centric approach, that full life cycle of data management, simplifying that, bringing the cloud experience to your data wherever it is. Could be "on-prem." >> Yeah. >> Could be in the cloud, sort of this API-based architecture, microservices, containers... >> Yep. >> All the kind of interesting buzzwords today, but they enable agility in a cloud-like experience, that Netflix-like experience that you were talking about. >> Absolutely, right, so we're super excited. The one thing I would also say is what our latest net backup, 812, the other thing that I talked about, which is simplicity and ease of use: we are addressing both of that in addition to the robust brand that we have around protecting data. So you now you have simplicity, ease of use, instant gratification, all the basic ingredients, and Veritas is here to protect them. >> Harish, it's been a great day. Thanks for helping me close out the segment here. This venue is really terrific. It's been a while since I've been at Tavern on the Green. Some of you guys, I don't think you've ever seen it before. Seth's down here; he's, he's a city boy but we country bumpkins up in Massachusetts, we love coming down here, in the heart of Yankee country. So thanks very much-- >> Of course. >> For helping me close out here, great segment. All right, thanks for watching, everybody. We're out here, from New York City, Tavern on the Green. You've been watching theCUBE; I'm Dave Vellante. We'll see you next time. (light electronic music)

(energetic music) >> Announcer: Live from New Orleans, Louisiana, it's The Cube! Covering .NEXT conference, 2018, brought to you by Nutanix. >> Welcome back to The Cube's coverage here of Nutanix .NEXT 2018, I'm Stu Miniman with my co-host, Keith Townsend. Happy to welcome to the program two first time guests. We have Sherry Lautenbach who's the SVP of America Sales with Nutanix and Inder Sidhu who is the EVP of Global Customer Success, also with Nutanix. Sherry and Inder, thanks for joining us. >> Sherry: Thank you. >> Alright, so Sherry, first of all, you were up on stage this morning celebrating customers, we actually had the chance yesterday to nominate one of the, to interview one of the, nominees there and talked about what that meant to them and it was really talked about, you know, it's validation, where you know, we're trying something, we think we went out beyond what other people are doing and getting that validation back was just, they were really excited just to be nominated, so, you know, take us inside. >> Yeah, so first of all, we had hundreds of nominations, so it was super hard to choose and break it down to the finalists and then of course the winners, but for us, it was about innovation about cloud trailblazers, you know, dev ops, lots of different types of awards this year, and recognizing things that customers are doing to innovate with Nutanix. The best award we did have was Art.Heart give-back award and that, you know, it says a lot about our company that we focus on what companies are doing to better the communities they live in and the world in general, so. >> Yeah, and JetBlue is the winner there. >> Absolutely. >> Have to say, it makes me even happier to talk about, I have status with JetBlue, cause I fly to a lot of shows. >> Yeah, I can imagine Doug, they've been a great partner of ours, a great spokesperson, and they've really leveraged our technology to innovate with their company, so it's been a, it was a great morning. >> Alright, Inder, we watched Nutanix since the early days, discussion about NPS scores, and when you can't, when you come to an event like this, you can't help but feel the passion of the customers - over 5500 people here. Talk to us about what your role is, your engagement with customers, that whole customer success, and what that means. >> Yeah, customer success in my mind, Stu, is probably the single most important thing that we do at Nutanix, and the reason is because customers drive everything that the company does; it drives our employee behavior, it drives our partner behavior, it drives our product roadmaps. We're an outside-in company, fundamentally, and therefore, driving the customer success holistically, not just in terms of support after they might have an issue, but holistically, end-to-end over the entire life cycle is very very important for us. So, we're creating an organization, an investment, reporting all the way to the CEO to drive exactly that and we're very excited about that. >> Right, and I call it customer obsession, so I've been at Nutanix six months, the first day I showed up to headquarters, they gave me my laptop, and then they brought me up to the customer support area and said, "This is why we're so successful, because we are maniacally focused on ensuring our customers are being delivered value every day." And with a focus on our NPS four daily. So, for me, that was super impressive, and we don't let up on it. >> Stu: You know, Sherry, and I love some of the pieces. You were talking about innovation, talking about developers-- >> Sherry: Yes. >> We've been talking to a lot of customers about their digital transformation. It's not just, "Oh, okay, I'm re-platforming," it's more than that, talking about, what one of the customers said is, you know, "Business as IT." >> Right, no absolutely. So, digital transformation is clearly the buzzword, but it is all about what are companies doing to transform their businesses to become digital. And, Dheeraj always says, you know, "To be in that digital transformation journey is all about what you do to transform not only your IT operations, but the business." And the business drives what digital transformation does, absolutely. And it's not just creating things online or creating a presence, but its actually innovating yourself to differentiate yourself from your competition. We've seen that time and time again on what Amazon did to bookstores or what Netflix did to Blockbuster. And those types of things are the innovation that drives the change. >> Keith: So, Inder, speaking of innovation-- >> Inder: Mmhmm. >> Nutanix digitally transformed themselves into a software company. You guys made a lot of announcements, a lot of new products in the pipeline, a lot of new features available: GA as of the show. Nutanix has become a bigger company, valuation over nine billion dollars, as you get bigger, it's hard to keep that NPS score over 90. Where's the focus and how do you do it as Nutanix grows? >> You know one of the things, I think, as we become a big company in terms of size and scale, in terms of our heart and in terms of our spirit, we're very much a small company. I go tell customers, there is going to be times when we'll screw up. But you'll never find any company that's going to work harder than us to drive your success. And that's where the intent is, that's where the focus is. We're going to do whatever it takes from an holistic end-to-end customer perspective. We're assigning customer success managers to some of our largest customers so we can proactively engage with them, especially along three dimensions. We're not like a lot of other technology companies, where you just try to sell them technology, we're around three things: we want to make sure make sure that our customers can be organizationally proficient, we want to make sure they're operationally efficient and we want to make sure that they're financially accountable. All three of those dimensions have to do with stuff that's important to them. As we make them successful along those dimensions, automatically the technology starts to get adopted and they start seeing some benefits. >> So, Sherry, let's talk about that customer success manager. What are they empowered to do, like, if there's a problem, how do they make it right? >> Well that's a great question, they're empowered to do whatever it takes on behalf of the customer to ensure that one, they're deploying our technology well and they're finding great value in it. It's interesting, I've spoken to many customers at this conference and so many of them have said, you know, using Nutanix has changed my career, my career trajectory, and the business value I provide the organization, not just from an IT standpoint, but on the business side. And so for me, there's no greater compliment when our customers, they're cheering for us, they're rooting for us cause we're helping to transform what they do every day. So the customer success manager is just going to be an overlap in terms of ensuring and driving that success as we get deeper and deeper into these customers. >> And what we're going to do is we're going to start out with customer success managers more at the top of the pyramid, some of the largest accounts, but remember, we still have hundreds and hundreds of account team members from Sherry's team and others; SEs, all of whom provide an even greater leverage, and then extending all the way through our partners. So we have a high-touch model at the top with CSMs, we have a medium-touch model with SEs and account teams and insight sales reps and partners in the middle, and on the bottom of the pyramid, we've got a tech-touch model, where we're going to actually leverage our technology with self-service portals and so on with emails and webinars and training and material that can actually drive their end-to-end success, very focused on that. >> Stu: Sherry, I'm wondering if you can dig in some of the organizational pieces that Inder was talking about. From your customers as you move up the food chain with the products, what are you hearing from your various constituencies inside of companies? >> Inside of our customers? >> Stu: Inside of the customers, yes. >> Right, so, well we cover, in terms of an organizational size, we cover all different types of customers in various ways. We have dedicated account people to our largest accounts alongside with SEs of course. And we leverage our partners, though, in our channel and everything we do, so they're considered an extension of our sales force, which I think is truly valuable and really important that we ensure that they drive success with our customers. >> Anything special you're hearing when you get up to the C-Suite, pain points, that they're hearing more than you heard in the architect or admin standpoint? >> Yeah, no, they're looking for more of, you know, helping to rationalize cloud: how do I get to cloud, what's the right balance in terms of hybrid, on-prem, off-prem, and really, understanding the business value and drivers around it, not just cost efficiency. It's about transforming different areas of their business and many of the C-Suite customers that I speak to really are approaching it many different ways, dependent on what is the key pain point and business problem they're trying to solve. >> Inder: So, two things I'd say to add to Sherry's answer there is that what we see is customers wanting to engage more architecturally rather than an individual point product through a consultative process that is more around business outcomes. So it's not something necessarily new, but it's a little bit new for Nutanix, cause we've historically engaged at the technology level, and now you're finding more and more. Of the Fortune 50, we have 33. Of the Fortune 100, we have 66. So we're actually starting to get to really large customers in a big way. They want a deeper, architectural, all-in engagement, and as our portfolio starts to expand from just HCI to Flow and Beam and Xi and all of those, they're saying gosh, I mean I just literally ran into a CIO in the elevator, coming down this morning, and he said gosh, we were thinking about doing NSX but now that I came here and I heard about Flow and I heard about Xi, I think I'm going to go all-in with you guys, I'm going to put that thing on ice, and really work with you guys on this. Literally, unsolicited, in the elevator, this morning. >> Keith: That's impressive. So as we, on all those lines of growth, you guys have a huge user community: 70,000 participants, and this morning, Dr. Brennan, I'm sorry, Dr. Brené Brown talked about having difficult conversations around diversity. I want to first give you guys kudos, this is from an optics perspective been one of the most diverse technology conferences I've attended from an entertainment to the onstage presence to the keynote speakers, awesome job. As you guys are working towards having a more diverse user set, how are you helping your user community be successful along with their careers from a diversity perspective and whereas a career development perspective. >> Great question, and yes, I'm super proud of the diversity, things we're doing in the company. Just yesterday, I hosted a women's IT luncheon, so we celebrated the women around Nutanix so that was all about building a network of all of our customers: female and male, they were included too in this luncheon. And we had over 130 people, spent time, I said let's exchange business cards, let's talk about some of the challenges you face. We had one of our board members, Sue Bostrom share some very personal stories about challenges she's faced and opportunities to help advance her career, gave a great perspective on that. We also had the CEO of FlyWheel, she talked about failing fast and pivoting, and that to me was great little lessons and tidbits that we can provide our customers to say let's empower you to be even better and to build your network even more effectively. >> And if I can add to that, I think, what we're always looking for is a diversity of ideas, and those diversity of ideas is not just a nice-to-have, it's a must-have because it actually drives positive business outcomes from us when we start to represent what our community of users and what our community of customers is. And that diversity of ideas comes from people who have had a diversity of backgrounds, across a wide range of dimensions of diversity, and that's what we're really looking for. We're not necessarily solving for outcomes, we want to solve for opportunity, and make sure that everybody has that equal opportunity to engage and participate, and the more we do that, the richer we get, the more powerful we get, the more alive we become, I think, with diversity. >> Right, I mean, you think about that, you know, our traditional influencer was in the data center side, but we've found now in terms of diversity of our portfolio, the developer is going to be just as important of an influencer for Nutanix, so we're looking at it from not only our customers and who but what they do. >> Stu: Inder, I was wondering if you could get some colla rosso on the vertical side of things, we know you started early very much in the public sector phase, had a lot of strength there, so speak to how else you're growing in the vertical space. >> Inder: Yeah, one of the things we're doing is as we get into bigger and larger customers, as you know, we have 9000 customers, adding a thousand every quarter, we have about 642 after global 2000 customers and so, as we get into those, those customers want us to be able to talk to them in their language, around their issue. So I'll give you a great example, you know, recently, we hired a guy, his name is Don Mims out of Baylor Scott & White as a Customer Success Manager. Here's a guy who's done everything the Nutanix products, implemented them all through Baylor Scott & White, 7000 beds, 48 hospitals, and here's a guy who's implemented Nutanix, he's implemented AHV, he's implemented Epic. I got 40 other customers in the US alone who want to implement Epic and AHV in the healthcare sector among the provider community, and we're going to go towards those customers with that kind of verticalized expertise. Same thing around financial services, same thing around retail. I mean, when you look at retail, Walmart, Home Depot, Tractor Supply Company, Nordstrom, Target, you know, Best Buy, Kohls, we've got a wide range of customers who give us insight into their operations, and when we engage with them, when you're talking to a retailer, you're talking about dollars per square foot, you're talking about same store sales, you're talking about a flexible workforce and then you translate that into IT, which translates into a hybrid public-private flexible infrastructure. So as we have these conversations, they're very engaging, and we are starting to verticalize if you will, in terms of our overlay expertise. Sales force of course is going to be geographic first, because of the proximity that's required, but we're going to have overlay both in the services and in the sales organization that's going to be very noticeable as well. >> And we have found that there are certain geographies and areas that we can verticalize in the field, so, for example, Tennessee or in California, we can build healthcare verticals which has been very effective cause customers want us to talk in their language, understand what critical business applications they can leverage with Nutanix. So we're trying to mirror, as best we can, the vertical point of view in the field. >> Public sector of course is the first vertical that gets carved out for many companies, service providers, the second, we've already got public sector carved out, and one of the things, great kudos to Sherry and her team, you were proactive, Sherry, with Brad Rhodes in kind of carving out healthcare as a dedicated sales region in the West where people have nowhere to hide, you just live and die by the healthcare success, customer success. >> Well, and also, the familiarity on the use cases, right, cause a lot of the use cases are repeatable, so it just makes a lot of sense for us to bring teams together that can go to market that way. >> Keith: So, let's talk about the speed of Nutanix. I love the story, the impromptu meeting, CIO in an elevator, you guys are wowing me with the technologies in ways I never thought of. Let's talk about the other end of it. Where are customers pushing you, saying, "You know what, you guys need to move faster." You have one customer that's on NSX, you have a bunch that are looking way past that. >> Sherry: Right, no that's a great question, and the great thing about Nutanix is we really don't say no a lot, I mean, we've got to be very thoughtful in what we sign up for, but we will innovate and collaborate with customers in every instance. So what is it that you need, you need a support on a platform? We'll give you the right timeframe to do it, but yeah, we're going to do what we can to deliver on that, so, there is a lot that's coming at us from a speed standpoint with our customers and the demands that they have but I think that's a testament to the adoption and the delight that they have of using Nutanix and wanting to expand that in their enterprise. >> Inder: And I think, to some extent, Keith, I think your question is more about where are we perhaps falling short a little bit, and I'll tell you one area where perhaps we could do better, which is for support of a wider array of platforms. So for example, when we go to Asia Pacific, a lot of our customers are telling us, gosh you got support for Dell or Lenovo or IBM, etc., but what about other platforms that are local, Hitachi or Fujitsu or Inspira or Avia, etc.? So we're going to get very disciplined and structured around it, we don't want to over commit and let anybody down, because extending support to multiple platforms is not trivial, but we want to make sure that when we commit, we say what we'll do and we do what we say. And that's a guarantee that we'd like to provide to our customers. >> Stu: Inder and Sherry, I want to give you both an opportunity: just final takeaways you want your customers to know about Nutanix as they leave the show this year. >> Well, we'd love for more customers to come onboard, one thing I've seen with our customers that are here is that they love our technology, they're delighted. We've helped change jobs and careers with many of our customers and for me that's a huge privilege. >> I'd just say that customer success is the single most important thing for us, for our customers, we might make a mistake every once in a while, but you will never find anybody who works harder on your behalf. We've got the energy, we've got the fire in the belly, we've got the agility, and we're going to do everything that it takes to make you successful, no matter what. Period, end of story. So we're all in, we hope you can be all in with us as well. >> Alright, Inder and Sherry, obviously the passion is here from you, from your customers and the team. Thanks so much for joining us today. For Keith Townsend, I'm Stu Miniman, lots more coverage here coming from Nutanix.NEXT, New Orleans, 2018. Thanks for watching The Cube. >> Thank you. (electronic music)

>> Narrator: Live from Las Vegas, it's theCube covering ServiceNow Knowledge 2018. Brought to you by ServiceNow. >> Welcome back everyone to theCube's live coverage of ServiceNow Knowledge18 here in Las Vegas. I'm your host Rebecca Knight along with my cohost Dave Vellante. We're joined by Dave Wright. He is the chief innovation officer at ServiceNow. Thanks so much for coming on the program. >> It's a pleasure, always a pleasure. >> Good to see you again Dave. >> Good to see you as well. >> Yeah, you've been around the block. You've been around theCube a few times. >> Around the block, a bad way of putting it but yeah. (laughing) >> So chief innovation officer, we've had a lot of great new product launches at this show. What are you most excited about, and what are you already thinking about when you go back to your office? >> So I think what's been interesting to me is the different way of engaging now, we've got the concept of virtual agent technology and I don't just mean the fact that we've got virtual agents. The fact that it starts to give people alternatives and it gives people alternative ways to come into the system, whether it be through our interface or whether it be through someone else's interface, I start to wonder, what'll happen going forward as we get more and more bot type technologies out. How will you have that one interface that works with all those to get that information back of the chain? How will you almost have a single interface that allows you to connect to all these bots and solve your problems? Because the benefits kind of two fold. One is the bot technology you get from being a customer to coming in and actually doing a request. But the other is you'll eventually be able to take that same technology and apply it to the fulfilled user so the power user because if I'm doing something and I can have an agent that's helping me do it, almost like the agent assist concept, you saw this morning. If I can take that to a next level and have AI running on top of that, then I can make work easier for the people coming in but I can actually improve the people that are in the system and make them more effective. >> Go ahead. >> Go ahead, follow up please. >> No, I was just going to ask about, how you get your ideas? So you're here, you're interacting with customers, you're seeing how they're using your product. So is it interviewing customers to find out their pain points? Is it really just watching, I mean you're the chief innovation officer. How do you spark your own creativity? >> It's a really weird answer. I get most of it off kids, most of it off my kids. So I can tell you a story. We were in Barnes and Noble the other week and they had albums in the, plastic twelve inch albums. >> Rebecca: They're coming back. >> And they cost more than they use to. >> Dave Vellante: Yeah really. >> So I called the kids over, I said look, let's get educated. This is what I use to play music on. And now we moved to CD's and you guys miss CD's and this is why you guys buy music. Now I've got a 12 year old and seven year old. And the 12 year old was saying, well, we don't buy music. And I said yeah, and I thought, no you don't, you rent music. And then my youngest daughter said, why would you want to own a song forever? And I was like, this is interesting. We started having a discussion, >> These are deep, these are deep questions. >> It was while other kids we're over having a sleepover and they're all eating pizza and they were talking about the concept of having a job. They said, how do you decide what you want to do for the rest of your life and how do you do that? And we we're talking about how you do something, you get better. You go to another company, you get better at doing it. You go to another company. And one of them said, it sounds really boring just like doing the same thing. And then one of them had the best answer. She said, don't you think it's a waste of your time? And I said, why is it a waste? And she said, because if you're really good at something, why should you just do it for one company? And I was like, oh so, you're going to be a contactor. (laughing) But what I realize was because this whole generation don't need to own things, they just need to use things, so they don't need to know how to do something, they just know they want to do it. And they don't need to own something, they just need temporary access to it. Then it got me thinking when you talk about where could work go to. Do you get a whole concept of the gig economy becoming a gig enterprise. Because we've got a lot of people in work who've got all these different skills but we force them to do one job. And it might be that someone's doing a job but they've got skills that would be applicable outside of that job but they never get to use them. So have we seen the first generation arrive now where they expect everything to be tass based? And then it gives you control over your own career. Because then you say, well, actually I'm not good at this but I can start a bid for work. I can say to people, hey I'm only a three on a skills racing but if you don't need any complex, I'll take it cause I get to learn. So it's a whole new dynamic and I think when you ask whereabout ideas come from, some of the first stage ideas or the first horizon, I think they come form customers. Some of the second horizon, they come from people who aren't working. It's just trying to imagine how they all develop and whereabout that all goes. >> So you surround yourself with millennials? >> Not even millennials. >> Dave Vellante: They're kind of pre post millennials. >> Almost like the linksters, almost the people who've been born connected. It's definitely a Gen Z thing but it's beyond millennials. I think the millennials had a certain expectation around well it's kind of a negative connotation but before they were called millennials, people use to refer to it as the entitlement generation. And it wasn't because they were entitled, it was because they felt they just got access to everything. So it's like with my kids, they're kind of Gen Z and one of them is a linkster, but you never go to them and say, they never come to you and say, hey, I want to watch a movie and you go, great, let's go to Blockbuster's, let's rent it. They expect it to be just available on demand, available all the time. And that was what I think the kind of millennial generation started being entitled to access to data, then I think you went to the generation where it was everything always connected, no concept of banword. But now I think it's the, the real thing that's changing is the concept of ownership and I think that's where you start to see things like, will the car industry ever be the same because if you don't need to own a car because you're not driven by the same passions that people who own cars are driven by, it's just a way of communicating you don't need a garage on your house, you may as well park the car somewhere else. It comes when you need it. It can change the way cities are laid out. I mean there's so many different routes you can go down with this. >> SO how does that innovation, how does that knowledge that you gain get into ServiceNow products and services? >> That all comes back then to how you, how people are going to face new management dynamics or how people are going to manage things like IOT devices? How are people going to deal with managing work if it is a task based economy? How are people going to start to think about not just working in a system of record, or not just working in a system of engagements, but how are they going to start to build that mesh or that web that links all these different things together? And I think that's where our strand comes. Our strand comes in the ability to be able to link systems of records together. To link users with those backend systems, to be able to manage those complex work processes. That's kind of the core elements. Also I think when you look at what Fred Crasick when he built the platform and he had the whole work flow engine and it is that engine that's kind of the key pallet to the whole company. >> I think the metaphor of mesh, sometimes we talk about a matrix of digital services that becomes ubiquitous beyond a cloud of remote services, is really transforming to this mesh of digital capabilities that are everywhere that do things that Clouds don't do. They sense, they react, they respond, they read, they hear. It's an amazing time that we're entering in innovation. Andy McAfee and Erik Brynjolfsson when they wrote the book Second Machine Age talked about Moore's Law, power innovation but they also talked about the innovation curve reshaping from going from Linears Moore's Law which we've marched to the cadence of Moore's Law for decades in this industry to reshaping, to an expediential curve. And I wonder if we could get your thoughts. We've paused that it's accommodation of sort of data applying machine intelligence to that data and then leveraging Cloud economics at scale is really where the innovation is going to come from in the future. What are your thoughts on that? >> So let me try to understand the question. So you're talking about not actually the way that you've seen the growth from a process prospective but the way you actually see the growth from a machine learning capability being able to analyze that data? >> Applying that layer of machine learning. Maybe use that mesh metaphor, that top level. You know you've got horizontal technology services but then there's this new AI layer on top. The data is the fuel for that AI. >> Absolutely, I think it's the I think people can't even imagine what they can do with that data, people can't even contemplate some of the decisions they can make and it's when people start to look at things in completely different ways, it's when people start to say, well, if we apply machine learning to a user interface for example, could we come up with a better user interface because now if we understand how people interact with the system, could we actually build a better system? Or you see people starting to have this whole butterfly effect around the way that artificial intelligence works. So the best example I heard was from, I was actually at a convention with a girl called Louis Chang and she was talking to me about it. But they were speaking to hospitals. They we're talking about self drive cars and the application machine learning of being able to help cars drive. And they were saying the interest in knock on effect of this was a hospital saying it was going to be a real problem for them having self drive cars. And she said, why's it going to be a problem? And the problem was, if you look across the whole America you have about 20 people a day die because they can't get replacement organs. But 37 percent of the organs come from car crashes. So if you take car crashes out of the equation. So what they were investing in was actually looking at how they do cloning technology for organs. So no one would ever imagine (mumbled speaking) and this is going to improve cloning technology so much. And I think AI's in the same place. Everyone's using it in such a small area that they don't even see the potential of what they could do with it, they don't have any concept of what they could be starting to look at and how they could start to spot transvaterian people. Even on a base level, I was speaking to one of our customers the other night, and they managed to put an AI system in place that when they got a call in off the description of the call they could work out what the customer satisfaction was going to be and if it was going to be a bad satisfaction figure, they could preemp that and put different agents that were more skilled on that particular issue. And they said a few years ago all they were interested in was maybe one day we'll be able to categorize something asymmetrically. But now they can predict how well something's going to be resolved. >> It's very hard to predict isn't it? I mean who would of thought that Alexa would of emerged as one of the best if not the best natural language processing systems or that images of cats on the internet would lead to facial recognition in technology. >> That one especially. >> Could of never predicted that. So, but because you're such a clear thinker and a strategic thinker, I want to ask you to make some predictions. I'm going to run some things by you. You talked about autonomous vehicles for awhile. Do you believe that owning in the future, pick whatever time frame you want, that owning and driving your own car will become the exception? >> Yeah I think it will probably be the people who, well okay, so I definitely think driving your own car will become the exception. I think some people will always want that sense of ownership until we get to a generation that doesn't. I think they'll always be a hard core of people who do want to own and do want to drive and do want that experience, but I think you've already got the issue where congestion's such a level in most areas. Is there any enjoyment out of driving? So I love driving, I love sports cars, I collect them. But if someone said, hey you've got two options, you can sit in a high performance sports car to go to LA or you can sit in a Tesla and it will drive itself and you can read a book. I'm getting in the Tesla. (laughing) >> How about retail? Right for disruption, do you think that large retail stores will essentially, not essentially, it's never complete, but will largely go away? >> I think it depends on the nature of the experience. So I think for a lot of goods that are consumable goods, I can kind of see that going away. I don't think it will go away for luxury goods. I don't think it will go away fully for fashion. I think people always like to look at things and understand things and check fits but for some things that are high consumables maybe even for electronics, I can see those going or I can see it going for things where it's worn product. So something like a shop that just sells sneakers. I can see someone could easily offer a range and say, well look, here's what we sell. When you order something, we'll automatically ship you one size up, one size down, or two variations of color and it will be a free system return the ones you don't want. I think the whole experience of ordering one thing and hoping it works out, I think that will go away. It will be concept of ordering a group of things or maybe it will be applying to artificial intelligence to say, hey you've asked for this color, but we know that people who also ask for that color like this color as well. We're going to ship you them both. You can see how it goes and send us the one back you don't like. >> Okay, let's see. Will machines make better diagnosis than doctors? I've got to say I think you will get to a point where that will happen. Especially if it's things where it's image processing, where it's x-ray processing, MRI processing. Where it's something like process of mental health, then I don't know. Maybe, I'd probably rather have my mental health treated by a person than a questionnaire. But yeah I think the things we're using, image recognition, or things where you're looking at patent distribution or you're looking at even like virus distribution or virus structure, then I think those areas I think you will get to a point where diagnostic issue is better. But you look at where artificial intelligence is from diagnostics now and you go on doctor google and search for something, you know, everything finished with the bottom line, or it could be cancer. >> Dave Vennari: Yeah, you're dead. >> What about will there ever be a revolt, you know in the sense of, technology is so pervasive, and people just say forget it, I'm sick of just being tracked, I just kind of want to have a human to human connection and, >> Dave Vellante: Dream on. >> So are we done for? >> I was speaking to a girl who works for me, Menesha, and she was saying, we were talking on Friday and she said, hey, I was having a coffee with nother girl Cass, and Menesha's in Seattle and Cass in is San Francisco, and I said, oh was she in Seattle or were you in San Francisco and Menesha's a lot younger than me, and she went, no we weren't in the same room. We were just like doing it over video like a virtual coffee. And I was like what, so you both get coffee and sit and have a conversation? And she was like, oh yeah. >> Dave Vellante: Alright, I've got one more, I've got one more. >> Okay, let's hear it, let's hear it. >> Alright last one, it's great, thanks for playing along. >> I know this is fun. >> Financial services is an industry that really hasn't been disrupted. DO you feel like the banks will lose control, the major banks will lose control of payment systems? >> I think there's a lot of conversations now around how much those payment systems open up. Because if you, let's say you do a transaction with Amazon, you do a transaction with Google, how hard would it be for every transaction to be done that way? So rather than, if your setting off a payment for I don't know, gas bills or a car loan payments, rather than giving your bank details, why not give your PayPal details or your Amazon account details or your Google details? That could be, reduce all the banking transactions down to one interface. I think that could happen. I think you could get, well obviously you're already seeing the rise of Blockchain and I'm not a Blockchain expert. I'm itching to find a used case for us with Blockchain but I can't find it yet. But for direct transactions, if both sources can trust each other than yeah, that direct transaction between those two sources, I think that's completely possible. I think there's also areas where, you've seen happen in the past where a banking faces issues from retail coming into banking, so sometimes you'll get the big supermarket chains, especially in Europe they say, okay you're going to get (foreign name) or you're going to get Tesco's Bank, because they've got all our customer loyalty, they've got people waiting to give discounts to if they bank with them, so they can instantly bring, if you said to your shopping account base, hey, if you bank with me we'll give you 20 dollars a week off your grocery shopping, you could probably ring 10 million customers straight away. So I think banking's challenged from other industries that want to get into it, from places where you'll actually go and do each transactions now and from where places where you'll just go and order stuff online and you could filter all that through one place, I think they'll still always be the commercial side of banking. There's always going to be the stocks and bonds, there's still going to be the wealth management, but props for transactional banking, you could start to see a decline. >> Fantastic, thank you. >> I love this futurist talk, it's been a lot of fun. Thank you so much for coming on theCube Dave. >> Alright, thanks for having me, always a pleasure. >> Dave Vellante: Great to see you. >> We will have more from ServiceNow Knowledge18 theCube's live coverage just after this. (upbeat music)

>> Hi, I'm Peter Burris, and welcome to another Wikibon Action Item. (funky electronic music) This was a very interesting week in the tech industry, specifically because IBM's Think Conference aggregated in a large number of people. Now, The CUBE was there. Dave Vellante, John Furrier, and myself all participated in somewhere in the vicinity of 60 or 70 interviews with thought leaders in the industry, including a number of very senior IBM executives. The reason why this becomes so important is because IBM made a proposal to the industry about how some of the digital disruption that the market faces is likely to unfold. The normal approach or the normal mindset that people have used is that startups, digital native companies were going to change the way that everything was going to operate, and the dinosaurs were going to go by the wayside. IBM's interesting proposal is that the dinosaurs actually are going to learn to dance, utilizing or playing on a book title from a number of years ago. And the specific argument was laid out by Ginni Rometty in her keynote, when she said that there are number of factors that are especially important here. Factor number one is that increasingly, businesses are going to recognize that the role that their data plays in competition is on the ascending. It's getting more important. Now, this is something that Wikibon's been arguing for quite some time. In fact, we have said that the whole key to digital disruption and digital business is to acknowledge the difference between business and digital business, is the role that data and data assets play in your business. So we have strong agreement there. But on top of that, Ginni Rometty made the observation that 80% of the data that could be accessed and put the work in business has not yet been made available to the new activities, the new processes that are essential to changing the way customers are engaged, businesses operate, and overall change and disruption occurs. So her suggestion is that that 80%, that vast amount of data that could be applied that's not being tapped, is embedded deep within the incumbents. And so the core argument from IBM is that the incumbent companies, not the digital natives, not the startups, but the incumbent companies are poised to make a significant, to have a significant role in disrupting how markets operate, because of the value of their data that hasn't currently been put to work and made available to new types of work. That was the thesis that we heard this week, and that's what we're going to talk about today. Are the incumbent really going to strike back? So Dave Vellante, let me start with you. You were at Think, you heard the same type of argument. What did you walk away with? >> So when I first heard the term incumbent disruptors, I was very skeptical, and I still am. But I like the concept and I like it a lot. So let me explain why I like it and why I think there's some real challenges. If I'm a large incumbent global 2,000, I'm not going to just roll over because the world is changing and software is eating my world. Rather what I'm going to do is I'm going to use my considerable assets to compete, and so that includes my customers, my employees, my ecosystem, the partnerships that I have there, et cetera. The reason why I'm skeptical is because incumbents aren't organized around their data assets. Their data assets are stovepipe, they're all over the place. And the skills to leverage that data value, monetize that data, understand the contribution that data makes toward monetization, those skills are limited. They're bespoke and they're very narrow. They're within lines of business or divisions. So there's a huge AI gap between the true digital business and an incumbent business. Now, I don't think all is lost. I think a lot of strategies can work, from M&A to transformation projects, joint ventures, spin-offs. Yeah, IBM gave some examples. They put up Verizon and American Airlines. I don't see them yet as the incumbent disruptors. But then there was another example of IBM Maersk doing some very interesting and disrupting things, Royal Bank of Canada doing some pretty interesting things. >> But in a joint venture forum, Dave, to your point, they specifically set up a joint venture that would be organized around this data, didn't they? >> Yes, and that's really the point I'm trying to make. All is not lost. There are certain things that you can do, many things that you can do as an incumbent. And it's really game on for the next wave of innovation. >> So we agree as a general principle that data is really important, David Floyer. And that's been our thesis for quite some time. But Ginni put something out there, Ginni Rometty put something out there. My good friend, Ginni Rometty, put something out there that 80% of the data that could be applied to disruption, better customer engagement, better operations, new markets, is not being utilized. What do we think about that? Is that number real? >> If you look at the data inside any organization, there's a lot of structured data. And that has better ability to move through an organization. Equally, there's a huge amount of unstructured data that goes in emails. It goes in voicemails, it goes in shared documents. It goes in diagrams, PowerPoints, et cetera, that also is data which is very much locked up in the way that Dave Vellante was talking about, locked up in a particular process or in a particular area. So is there a large amount of data that could be used inside an organization? Is it private, is it theirs? Yes, there is. The question is, how do you tap that data? How do you organize around that data to release it? >> So this is kind of a chicken and egg kind of a problem. Neil Raden, I'm going to turn to you. When we think about this chicken and egg problem, the question is do we organize in anticipation of creating these assets? Do we establish new processes in anticipation of creating these data assets? Or do we create the data assets first and then re-institutionalize the work? And the reason why it's a chicken and egg kind of problem is because it takes an enormous amount of leadership will to affect the way a business works before the asset's in place. But it's unclear that we're going to get the asset that we want unless we affect the reorganization, institutionalization. Neil, is it going to be a chicken? Is it going to be the egg? Or is this one of the biggest problems that these guys are going to have? >> Well, I'm a little skeptical about this 80% number. I need some convincing before I comment on that. But I would rather see, when David mentioned the PowerPoint slides or email or that sort of thing, I would rather see that information curated by the application itself, rather than dragged out in broad data and reinterpreted in something. I think that's very dangerous. I think we saw that in data warehousing. (mumbling) But when you look at building data lakes, you throw all this stuff into a data lake. And then after the fact, somebody has to say, "Well, what does this data mean?" So I find it kind of a problem. >> So Jim Kobielus, a couple weeks ago Microsoft actually introduced a technology or a toolkit that could in fact be applied to move this kind of advance processing for dragging value out of a PowerPoint or a Word document or something else, close and proximate to the application. Is that, I mean, what Neil just suggested I think is a very, very good point. Are we going to see these kinds of new technologies directly embedded within applications to help users narrowly, but businesses more broadly, lift that information out of these applications so it can be freed up for other uses? >> I think yeah, on some level, Peter, this is a topic called dark data. It's been discussed in data management circles for a long time. The vast majority, I think 75 to 80% is the number that I see in the research, is locked up in terms of it's not searchable, it's not easily discoverable. It's not mashupable, I'm making up a word. But the term mashup hasn't been used in years, but I think it's a good one. What it's all about is if we want to make the most out of our incumbent's data, then we need to give the business, the business people, the tools to find the data where it is, to mash it up into new forms and analytics and so forth, in order to monetize it and sell it, make money off of it. So there are a wide range of data discovery and other tools that support a fairly self-service combination and composition of composite data object. I don't know that, however, that the culture of monetizing existing dataset and pulling dark data into productized forms, I don't think that's taken root in any organization anywhere. I think that's just something that consultants talk about as something that gee, should be done, but I don't think it's happening in the real world. >> And I think you're probably correct about that, but I still think Neil raised a great point. And I would expect, and I think we all believe, that increasingly this is not going to come as a result of massive changes in adoption of new data science, like practices everywhere, but an embedding of these technologies. Machine learning algorithms, approaches to finding patterns within application data, in the applications themselves, which is exactly what Neil was saying. So I think that what we're going to see, and I wanted some validation from you guys about this, is increasingly tools being used by application providers to reveal data that's in applications, and not open source, independent tool chains that then ex-post-facto get applied to all kinds of different data sources in an attempt for the organization to pull the stuff out. David Floyer, what do you think? >> I agree with you. I think there's a great opportunity for the IT industry in this area to put together solutions which can go and fit in. On the basis of existing applications, there's a huge amount of potential, for example, of ERP systems to link in with IOT systems, for example, and provide a data across an organization. Rather than designing your own IOT system, I think people are going to buy-in pre-made ones. They're going to put the devices in, the data's going to come in, and the AI work will be done as part of that, as part of implementing that. And right across the board, there is tremendous opportunity to improve the applications that currently exist, or put in new versions of applications to address this question of data sharing across an organization. >> Yeah, I think that's going to be a big piece of what happens. And it also says, Neil Raden, something about whether or not enormous machine learning deities in the sky, some of which might start with the letter W, are going to be the best and only way to unlock this data. Is this going to be something that, we're suggesting now that it's something that's going to be increasingly-distributed closer to applications, less invasive and disruptive to people, more invasive and disruptive to the applications and the systems that are in place. And what do you think, Neil? Is that a better way of thinking about this? >> Yeah, let me give you an example. Data science the way it's been practiced is a mess. You have one person who's trying to find the data, trying to understand the data, complete your selection, designing experiments, doing runs, and so forth, coming up with formulas and then putting them in the cluster with funny names so they can try to remember which one was which. And now what you have are a number of software companies who've come up with brilliant ways of managing that process, of really helping the data science to create a work process in curating the data and so forth. So if you want to know something about this particular model, you don't have to go to the person and say, "Why did you do that model? "What exactly were you thinking?" That information would be available right there in the workbench. And I think that's a good model for, frankly, everything. >> So let's-- >> Development pipeline toolkits. That's a hot theme. >> Yeah, it's a very hot theme. But Jim, I don't think you think but I'm going to test it. I don't think we're going to see AI pipeline toolkits be immediately or be accessed by your average end user who's putting together a contract, so that that toolkit or so that data is automatically munched and ingested or ingested and munched by some AI pipeline. This is going to happen in an application. So the person's going to continue to do their work, and then the tooling will or will not grab that information and then combine it with other things through the application itself into the pipeline. We got that right? >> Yeah, but I think this is all being, everything you described is being embedded in applications that are making calls to backend cloud services that have themselves been built by data scientists and exposed through rest APIs. Steve, Peter, everything you're describing is coming to applications fairly rapidly. >> I think that's a good point, but I want to test it. I want to test that. So Ralph Finos, you've been paying a lot of attention during reporting season to what some of the big guys are saying on some of their calls and in some of their public statements. One company in particular, Oracle, has been finessing a transformation, shall we say? What are they saying about how this is going as we think about their customer base, the transformation of their customer base, and the degree to which applications are or are not playing a role in those transformations? >> Yeah, I think in their last earnings call a couple days ago that the point that they were making around the decline and the-- >> Again, this is Oracle. So in Oracle's last earnings call, yeah. >> Yeah, I'm sorry, yeah. And the decline and the revenue growth rate in the public cloud, the SAS end of their business, was a function really of a slowdown of the original acquisitions they made to kind of show up as being a transformative cloud vendor, and that are basically beginning to run out of gas. And I think if you're looking at marketing applications and sales-related applications and content-type of applications, those are kind of hitting a natural high of growth. And I think what they were saying is that from a migration perspective on ERP, that that's going to take a while to get done. They were saying something like 10 or 15% of their customer base had just begun doing some sort of migration. And that's a data around ERP and those kinds of applications. So it's a long slog ahead of them, but I'd rather be in their shoes, I think, for the long run than trying to kind of jazz up in the near-term some kind of pseudo-SAS cloud growth based on acquisition and low-lying fruit. >> Yeah, because they have a public cloud, right? I mean, at least they're in the game. >> Yeah, and they have to show they're in the game. >> Yeah, and specifically they're talking about their applications as clouds themselves. So they're not just saying here's a set of resources that you can build, too. They're saying here's a set of SAS-based applications that you can build around. >> Dave: Right. Go ahead, Ralph, sorry. >> Yeah, yeah. And I think the notion there is the migration to their ERP and their systems of record applications that they're saying, this is going to take a long time for people to do that migration because of complexity in process. >> So the last point, or Dave Vellante, did you have a point you want to make before I jump into a new thought here? >> I just compare and contrast IBM and Oracle. They have public clouds, they have SAS. Many others don't. I think this is a major different point of differentiation. >> Alright, so we've talked about whether or not this notion of data as a source of value's important, and we agree it is. We still don't know whether or not 80% is the right number, but it is some large number that's currently not being utilized and applied to work differently than the data currently is. And that likely creates some significant opportunities for transformation. Do we ultimately think that the incumbents, again, I mention the chicken and the egg problem. Do we ultimately think that the incumbents are... Is this going to be a test of whether or not the incumbents are going to be around in 10 years? The degree to which they enact the types of transformation we thought about. Dave Vellante, you said you were skeptical. You heard the story. We've had the conversation. Will incumbents who do this in fact be in a better position? >> Well, incumbents that do take action absolutely will be in a better position. But I think that's the real question. I personally believe that every industry is going to get disrupted by digital, and I think a lot of companies are not prepared for this and are going to be in deep trouble. >> Alright, so one more thought, because we're talking about industries overall. There's so many elements we haven't gotten to, but there's one absolute thing I want to talk about. Specifically the difference between B2C and B2B companies. Clearly the B2C industries have been disrupted, many of them pretty significantly, over the last few years. Not too long ago, I have multiple not-necessarily-good memories of running the aisles of Toys R Us sometime after 10 o'clock at night, right around December 24th. I can't do that anymore, and it's not because my kids are grown. Or I won't be able to do that soon anymore. So B2C industries seem to have been moved faster, because the digital natives are able to take advantage of the fact that a lot of these B2C industries did not have direct and strong relationships with those customers. I would posit that a lot of the B2B industries are really where the action's going to take. And the kind of way I would think about it, and David Floyer, I'll turn to you first. The way I would think about it is that in the B2C world, it's new markets and new ways of doing things, which is where the disruption's going to take place. So more of a substitution as opposed to a churn. But in the B2B markets, it's disrupting greater efficiencies, greater automation, greater engagement with existing customers, as well as finding new businesses and opportunities. What do you think about that? >> I think the B2B market is much more stable. Relationships, business relationships, very, very important. They take a long time to change. >> Peter: But much of that isn't digital. >> A lot of that is not digital. I agree with that. However, I think that the underlying change that's happening is one of automation. B2B are struggling to put into place automation with robots, automation everywhere. What you see, for example, in Amazon is a dedication to automation, to making things more efficient. And I think that's, to me, the biggest challenges, owning up to the fact that they have to change their automation, get themselves far more efficient. And if they don't succeed in doing that, then their ability to survive or their likelihood of being taken over with a reverse takeover becomes higher and higher and higher. So how do you go about that level, huge increase in automation that is needed to survive, I think is the biggest question for B2B players. >> And when we think about automation, David Floyer, we're not talking about the manufacturing arms or only talking about the manufacturing arms. We're talking about a lot of new software automation. Dave Vellante, Jim Kobielus, RPA is kind of a new thing. Dave, we saw some interesting things at Think. Bring us up to speed quickly on what the community at Think was talking about with RPA. >> Well, I tell you. There were a lot of people in financial services, which is IBM's stronghold. And they're using software robots to automate a lot of the backend stuff that humans were doing. That's a major, major use case. I would say 25 to 30% of the financial services organizations that I talked to had active RPA projects ongoing at the moment. I don't know. Jim, what are your thoughts? >> Yeah, I think backend automation is where B2B disruption is happening. As the organizations are able to automate more of their backend, digitize more of their backend functions and accelerate them and improve the throughput of transactions, are those that will clean up. I think for the B2C space, it's the frontend automation of the digitalization of the engagement channels. But RPA is essentially a key that's unlocking backend automation for everybody, because it allows more of the frontend business analysts and those who are not traditionally BPM, or business process re-engineering professionals, to begin to take standard administrative processes and begin to automate them from, as it were, the outside-in in a greater way. So I think RPA is a secret key for that. I think we'll see some of the more disruptive organizations, businesses, take RPA and use it to essentially just reverse-engineer, as it were, existing processes, but in an automated fashion, and drive that improvement but in the backend by AI. >> I just love the term software robots. I just think that that's, I think that so strongly evokes what's going to happen here. >> If I could add, I think there's a huge need to simplify that space. The other thing I witnessed at IBM Think is it's still pretty complicated. It's still a heavy lift. There's a lot of big services component to this, which is probably why IBM loves it. But there's a massive market, I think, to simplify the adoption or RPA. >> I completely agree. We have to open the aperture as well. Again, the goal is not to train people new things, new data science, new automation stuff, but to provide tools and increasingly embed those tools into stuff that people are already using, so that the disruption and the changes happen more as a consequence of continuing to do what the people do. Alright, so let's hit the action item we're on, guys. It's been a great conversation. Again, we haven't talked about GDPR. We haven't talked about a wide array of different factors that are going to be an issue. I think this is something we're going to talk about. But on the narrow issue of can the disruptors strike back? Neil Raden, let's start with you. Neil Raden, action item. >> I've been saying since 1975 that I should be hanging around with a better class of people, but I do spend a lot of time in the insurance industry. And I have been getting a consensus that in the next five to 10 years, there will no longer be underwriters for claims adjustments. That business is ready for massive, massive change. >> And those are disruptors, largely. Jim Kobielus, action item. >> Action item. In terms of business disruption, is just not to imagine that because you were the incumbent in the past era in some solution category that's declining, that that automatically guarantees you, that makes your data fit for seizing opportunities in the future. As we've learned from Blockbuster Video, the fact that they had all this customer data didn't give them any defenses against Netflix coming along and cleaning their coffin, putting them out of business. So the next generation of disruptor will not have any legacy data to work from, and they'll be able to work miracles because they made a strategic bet on some frontend digital channel that made all the difference. >> Ralph Finos, action item. >> Yeah, I think there's a notion here of siege mentality. And I think the incumbents are in the castle walls, and the disruptors are outside the castle walls. And sometimes the disruptors, you know, scale the walls. Sometimes they don't. But I think being inside the walls is a long-run tougher thing to be at. >> Dave Vellante, action item. >> I want to pick up on something Neil said. I think it's alluring for some of these industries, like insurance and financial services and healthcare, even parts of government, that really haven't been disrupted in a huge way yet to say, "Well, I'll wait and I'll see what happens." I think that's a huge mistake. I think you have to start immediately thinking about strategies, particularly around your data, as we talked about earlier. Maybe it's M&A, maybe it's joint ventures, maybe it's spinning out new companies. But the time is past where you should be acting. >> David Floyer, action item. >> I think that it's easier to focus on something that you can actually do. So my action item is that the focus of most B2B companies should be looking at all of their processes and incrementally automating them, taking out the people cost, taking out the cost, other costs, automating those processes as much as possible. That, in my opinion, is the most likely path to being in the position that you can continue to be competitive. Without that focus, it's likely that you're going to be disrupted. >> Alright. So the one thing I'll say about that, David, is when I think you say people cost I think you mean the administrative cost associated with people. >> And people doing things, automating jobs. >> Alright, so we have been talking here in today's Wikibon Action Item about the question, will the incumbents be able to strike back? The argument we heard at IBM Think this past week, and this is the third week of March, was that data is an asset that can be applied to significantly disrupt industries, and that incumbents have a lot of data that hasn't been bought into play in the disruptive flow. And IBM's argument is that we're going to see a lot of incumbents start putting their data into play, more of their data assets into play. And that's going to have a significant impact ultimately on industry structure, customer engagement, the nature of the products and services that are available over the course of the next decade. We agree. We generally agree. We might nitpick about whether it's 80%, whether it's 60%. But in general, the observation is an enormous amount of data that exists within a large company, that's related to how they conduct business, is siloed and locked away and is used once and is not made available, is dark and is not made available for derivative uses. That could, in fact, lead to significant consequential improvements in how a business's transaction costs are ultimately distributed. Automation's going to be a big deal. David Floyer's mentioned this in the past. I'm also of the opinion that there's going to be a lot of new opportunities for revenue enhancement and products. I think that's going to be as big, but it's very clear that to start it makes an enormous amount of sense to take a look at where your existing transaction costs are, where existing information asymmetries exist, and see what you can do to unlock that data, make it available to other processes, and start to do a better job of automating local and specific to those activities. And we generally ask our clients to take a look at what is your value proposition? What are the outcomes that are necessary for that value proposition? What activities are most important to creating those outcomes? And then find those that, by doing a better job of unlocking new data, you can better automate those activities. In general, our belief is that there's a significant difference between B2C and B2B businesses. Why? Because a lot of B2C businesses never really had that direct connection, therefore never really had as much of the market and customer data about what was going on. A lot of point-of-sale perhaps, but not a lot of other types of data. And then the disruptors stepped in and created direct relationships, gathered that data and were able to rapidly innovate products and services that served consumers differently. Where a lot of that new opportunity exists is in the B2B world. And here's where the real incumbents are going to start flexing their muscles over the course of the next decade, as they find those opportunities to engage differently, to automate existing practices and activities, change their cost model, and introduce new approaches to operating that are cloud-based, blockchain-based, data-based, based on data, and find new ways to utilize their people. If there's one big caution we have about this, it's this. Ultimately, the tooling is not broadly mature. The people necessary to build a lot of these tools are increasingly moving into the traditional disruptors, the legacy disruptors if we will. AWS, Netflix, Microsoft, companies more along those lines. That talent is very dear still in the industry, and it's going to require an enormous effort to bring those new types of technologies that can in fact liberate some of this data. We looked at things like RPA, robot process automation. We look at the big application providers to increasingly imbue their products and services with some of these new technologies. And ultimately, paradoxically perhaps, we look for the incumbent disruptors to find ways to disrupt without disrupting their own employees and customers. So embedding more of these new technologies in an ethical way directly into the systems and applications that serve people, so that the people face minimal changes to learning new tricks, because the systems themselves have gotten much more automated and much more... Are able to learn and evolve and adjust much more rapidly in a way that still corresponds to the way people do work. So our action item. Any company in the B2B space that is waiting for data to emerge as an asset in their business, so that they can then do all the institutional, re-institutionalizing of work and reorganizing of work and new types of investment, is not going to be in business in 10 years. Or it's going to have a very tough time with it. The big challenge for the board and the CIO, and it's not successfully been done in the past, at least not too often, is to start the process today without necessarily having access to the data, of starting to think about how the work's going to change, think about the way their organization's going to have to be set up. This is not business process re-engineering. This is organizing around future value of data, the options that data can create, and employ that approach to start doing local automation, serve customers, and change the way partnerships work, and ultimately plan out for an extended period of time how their digital business is going to evolve. Once again, I want to thank David Floyer here in the studio with me. Neil Raden, Dave Vellante, Ralph Finos, Jim Kobielus remote. Thanks very much guys. For all of our clients, once again this has been a Wikibon Action Item. We'll talk to you again. Thanks for watching. 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>> Announcer: Live from San Jose, it's The Cube, presenting Big Data, Silicon Valley. Brought to you by Silicon Angle Media and its ecosystem partners. >> Welcome back to The Cube. Our continuing coverage of our event, Big Data SV continues. I am Lisa Martin joined by Peter Burris. We're in downtown San Jose at a really cool place called Forager Tasting and Eatery. Come down, hang out with us today as we have continued conversations around all things big data, everything in between. This is our second day here and we're excited to welcome to The Cube the CMO of VANTIQ, Blaine Mathieu. Blaine, great to meet you, great to have you on the program. >> Great to be here, thanks for inviting me. >> So, VANTIQ, you guys are up the street in Walnut Creek. What do you guys do, what are you about, what makes VANTIQ different? >> Well, in a nutshell, VANTIQ is a so called high productivity application development platform to allow developers to build, deploy, and manage so called event driven real time applications, the kind of applications that are critical for driving many of the digital transformation initiatives that enterprises are trying to get on top of these days. >> Digital trasformation, it's a term that can mean so many different things, but today, it's essential for companies to be able to compete, especially enterprise companies with newer companies that are more agile, more modern. But if we peel apart digital transformation, there's so many elements that are essential. How do you guys help companies, enterprises, say, evolve their application architectures that might currently not be able to support an actual transformation to a digital business? >> Well, I think that's a great question, thank you. I think the key to digital trasformation is really a lot around the concept of real time, okay. The reason Uber is disrupting or has disrupted the taxi industry is the old way of doing it was somebody called a taxi and then they waited 30 minutes for a taxi to show up and then they told the taxi where to go and hopefully they got there. Whereas, Uber, turned that into a real time business, right? You called, you pinged something on your phone. They knew your location. They knew the location of the driver. They matched those up, brought 'em together in real time. Already knew where to bring you to and ensured you had the right route and that location. All of this data flowing, all of these actions have been taken in real time. The same thing applies to a disruptor like Netflix, okay? In the old days, Blockbuster used to send you, you know, a leaflet in the mail telling you what the new movies are. Maybe it was personalized for you. Probably not. No, Netflix knows who you are instantly, gives you that information, again, in real time based on what you've done in the past and is able to give you, deliver the movie also, in real time pretty well. Every disruptor you look at around digital transformation is bringing a business or a process that was done slowly and impersonally to make it happen in real time. Unfortunately, enterprise applications and the architectures, as you said a second ago, that are being used in most applications today weren't designed to enable these real time use cases. A great example is sales force. So, a sales force is a pretty standard, what you'd call a request application. So, you make a request, a person, generally, makes a request of the system, system goes into a database, queries that database, find information and then returns it back to the user. And that whole process could take, you know, significant amounts of time, especially if the right data isn't in the database at the time and you have to go request it or find it or create it. A new type of application needs to be created that's not fundamentally database centric, but it's able to take these real time data streams coming in from devices, from people, from enterprise systems, process them in real time and then take an action. >> So, let's pretend I'm a CEO. >> Yeah. >> One of the key things you said, and I want you to explain it better, is event. What is event? What is an event and how does that translate into a digital business decision? >> This notion of complex event processing CEP has been around in technology for a long time and yet, it surprises me still a lot of folks we talk to, CEOs, have never heard of the concept. And, it's very simple really. An event is just something that happens in the context of business. That's as complex and as simple as it is. An event could be a machine increases in temperature by one degree, a car moves from one location to another location. It could be an enterprise system, like an ERP system, you know, approves a PO. It could be a person pressing a button on a mobile device. All of those, or it could be an IOT device putting off a signal about the state of a machine. Increasingly, we're getting a lot of events coming from IOT devices. So, really, any particular interesting business situation or a change in a situation that happens is an event And increasingly driven, as you know, by IOT, by augmented reality, by AI and machine learning, by autonomous vehicles, by all these new real time technologies are spinning off more and more events, streams of these events coming off in rapid fashion and we have to be able to do something about them. >> Let me take a crack at it and you tell me if I've got this right. That, historically, applications have been defined in terms of processes and so, in many respects, there was a very concrete, discreet, well established program, set of steps that were performed and then the transaction took place. And event, it seems to me is, yeah, we generally described it, but it changes in response to the data. >> Right, right. >> So, an event is kind of like an outside in driven by data. >> Right, right. >> System response, whereas, your traditional transaction processing is an inside out driven by a sequence of programmed steps, and that decision might have been made six years ago. So, the event is what's happening right now informed by data versus a transaction, traditional transaction is much more, what did we decide to do six years ago and it just gets sustained. Have I got that right? >> That's right. Absolutely right or six hours ago or even six minutes ago, which might seem wow, six minutes, that's pretty good, but take a use case for a field service agent trying to fix a machine or an air conditioner on top of a building. In today's world now, that air conditioner has hundreds of sensors that are putting off data about the state of that air conditioner in real time. A service tech has the ability to, while the machine is still putting off that data, be able to make repairs and changes and fixes, again, in the moment, see how that is changing the data coming off the machine, and then, continue to make the appropriate repairs in collaboration with a smart system or an application that's helping them. >> That's how identifying patterns about what the problem is, versus some of the old ways was where we had recipe of, you know, steps that you went through in the call center. >> Right, right. And the customer is getting more and more frustrated. >> They got their clipboard out and had the 52 steps they followed to see oh that didn't work, now the next step. No, data can help us do that much more efficiently and effectively if we're able to process it in real time. >> So, in many respects, what we're really talking about is an application world or a world looking forward where the applications, which historically have been very siloed, process driven, to a world where the application function is much more networked together and the application, the output of one application is having a significant impact through data on the performance of an application somewhere else. That seems like it's got the potential to be an extremely complex fabric. (laughing) So, do I wait until I figure all that out (laughing) and then I start building it? Or do I, I mean, how do I do it? Do I start small and create and grow into it? What's the best way for people to start working on this? >> Well, you're absolutely right. Building these complex, geeking out a little bit, you know, asynchronous, non-blocking, so called reactive applications, that's the concept that we've been using in computer science for some time, is very hard, frankly. Okay, it's much easier to build computing systems that process things step one, step, two, step three, in order, but if you have to build a system that is able to take real time inputs or changes at any point in the process at any time and go in a different direction, it's very complex. And, computer scientists have been writing applications like this for decades. It's possible to do, but that isn't possible to do at the speed that companies now want to transform themselves, right? By the time you spec out an application and spend two years writing it, your business competitors have already disrupted you. The requirements have already changed. You need to be much more rapid and agile. And so, the secret sauce to this whole thing is to be able to write these transformative applications or create them, not even write is actually the wrong word to use, to be able to create them. >> Generate them. >> Yeah, generate them in a way which is very fast, does not require a guru level developer and reactive Java or some super low level code that you'd have to use to otherwise do it, so that you can literally have business people help design the applications, conceptually build them almost in real time, get them out into the market, and then be able to modify them as you need to, you know, on the fly. >> If I can build on that for just one second. So, it used to be we had this thing called computer assisted software engineer. >> (laughs) Right, right. >> We were going to operate this very very high level language. It's kind of-- But then, we would use code and build a code and the two of them were separated and so the minute that we deployed, somebody would go off and maintain and the whole thing would break. >> Right, right. >> Do you have that problem? >> No, well, that's exactly right. So, the old, you know, the old, the previous way of doing it was about really modeling an application, maybe visually, drag and drop, but then fundamentally, you created a bunch of code and then your job, as you said after, was to maintain and deploy and manage. >> Try to sustain some connection back up to that beautiful visual model. >> And you probably didn't because that was too much. That was too much work, so forget about the model after that. Instead, what we're able to do these days is to build the applications visually, you know, really for the most part with either super low code or, in many cases, no code because we have the ability to abstract away a lot of the complexity, a lot of the complex code that you'd have to write, we can represent that, okay, with these logical abstractions, create the applications themselves, and then continue to maintain, add to, modify the application using the exact same structure. You're not now stuck on, now you're stuck with 20,000 lines of code that you have to, that you have to edit. You're continuing to run and maintain the application just the way you built it, okay. We've now got to the place in computer science where we can actually do these things. We couldn't do them, you know, 20 years ago with case, but we can absolutely do them now. >> So, I'm hearing from a customer internal perspective a lot of operational efficiencies that VANTIQ can drive. Let's look now from a customer's perspective. What are the business impacts you're able to make? You mentioned the word reactive a minute ago when you were talking about applications, but do you have an example where you've, VANTIQ, has enabled a customer, a business, to be more, to be proactive and be able to identify through, you know, complex event processing, what their customers are doing to be able to deliver relevant messages and really drive revenue, drive profit? >> Right, right. So many, you know, so many great examples. And, I mentioned field service a few minutes ago. I've got a lot of clients in that doing this real time field service using these event processing applications. One that I want to bring up right now is one of the largest global shoe manufacturers, actually, that's a client of VANTIQ. I, unfortunately, can't say the name right now 'cause they want to keep what they're doing under wraps, but we all definitely know the company. And they're using this to manage the security, primarily, around their real time global supply chain. So, they've got a big challenge with companies in different countries redirecting shipments of their shoes, selling them on the gray market, at different prices than what are allowed in different regions of the world. And so, through both sensorizing the packages, the barcode scanning, the enterprise systems bringing all that data together in real time, they can literally tell in the moment is something is be-- If a package is redirected to the wrong region or if literally a shoe or a box of shoes is being sold where it shouldn't be sold at the wrong price. They used to get a monthly report on the activities and then they would go and investigate what happened last month. Now, their fraud detection manager is literally sitting there getting this in real time, saying, oh, Singapore sold a pallet of shoes that they should not have been able to sell five minute ago. Call up the guy in Singapore and have him go down and see what's going on and fix that issue. That's pretty powerful when you think about it. >> Definitely, so like reduction in fraud or increase in fraud detection. Sounds like, too, there's a potential for a significant amount of cost savings to the business, not just meeting the external customer needs, but from a, from a cost perspective reduction. Not just some probably TCO, but in operational expenses. >> For sure, although, I would say most of the digital transformation initiatives, when we talk to CEOs and CIOs, they're not focused as much on cost savings, as they're focused on A, avoiding being disrupted by the next interesting startup, B, creating new lines of business, new revenue streams, finding out a way to do something differently dramatically better than they're currently doing it. It's not only about optimizing or squeezing some cost out of their current application. This thing that we are talking about, I guess you could say it's an improvement on their current process, but really, it's actually something they just weren't even really doing before. Just a total different way of doing fraud detection and managing their global supply chain that they just fundamentally weren't even doing. And now, of course, they're looking at many other use cases across the company, not just in supply chain, but, you know, smart manufacturing, so many use cases. Your point about savings, though, there's, you know, what value does the application itself bring? Then, there's the question of what does it cost to build and maintain and deploy the application itself, right? And, again, with these new visual development tools, they're not modeling tools, you're literally developing the application visually. You know, I've been in so many scenarios where we talked to large enterprises. You know, we talk about what we're doing, like we talk about right now, and they say, okay, we'd love to do a POC, proof of concept. We want to allocate six months for this POC, like normally you would probably do for building most enterprise applications. And, we inevitably say, well, how about Friday? How about we have the POC done by Friday? And, you know, we get the Germans laugh, you know, laugh uncomfortably and we go away and deliver the POC by Friday because of how much different it is to build applications this way versus writing low level Java or C-sharp code and sticking together a bunch of technologies and tools 'cause we abstract all that away. And, you know, the eyes drop open and the mouth drops open and it's incredible what modern technology can do to radically change how software is being developed. >> Wow, big impact in a short period of time. That's always a nice thing to be able to deliver. >> It is, it is to-- It's great to be able to surprise people like that. >> Exactly, exactly. Well, Blaine, thank you so much for stopping by, sharing what VANTIQ is doing to help companies be disruptive and for sharing those great customer examples. We appreciate your time. >> You're welcome. Appreciate the time. >> And for my co-host, Peter Burris, I'm Lisa Martin. You're watching The Cube's continuing coverage of our event, Big Data SV Live from San Jose, down the street from the Strata Data Conference. Stick around, we'll be right back with our next guest after a short breal. (techy music)

(uptempo orchestral music) >> Hello and welcome to a special exclusive conversation here in the studios of Palo Alto, California. I'm John Furrier, your co-host and theCube co-founder of Silicon Angle Media. We have exclusive, breaking launch here from a Cube alumni Al Burgio, who's the founder and CEO of Fusechain, a hot start up going after the blockchain, a little bit of open source. This is a launch. This is new information coming out. You still (indistinct talking) for the first time talking about your project again Cube alumni. Welcome to the theCube conversation. >> Thank you for having me John. >> You're the founder and CEO of Fusechain. >> That's correct. >> So you're just in Miami, 5000 people at these blockchain conferences which are exploded the biggest wave. Crypto and Blockchain in tandem are creating a very attractive and intoxicating market. It's the biggest wave we've seen in all the alpha entrepreneurs going out there. Some scammers too are trying to get into this market. We've documented that on theCube. But it's the biggest wave we've seen in a long time. You're out there. Talk about what is Fusechain? What's the story? Gives us the update. >> Sure. So Fusechain is a blockchain technology company, really founded to support a new open source project that is also coming out of stealth mode called the digital bits project. It's focused on disrupting the coalition loyalty industry. What we refer to as let's say one dot of loyalty in rewards. We feel that that market is ripe for disruption. A lot of frictions, others I'm happy to talk about in that space and we feel that blockchain in a decentralized model with the right partners and coalition could change the game. >> So you've got a T-shirt for us. I appreciate it called digital bits. New open source project. What I like about what you're doing, first of all you got a great track record. You have a ton of start ups you've done in the past and again great exits and you always have a good eye for where there's disruption and certainly crypto is dislocating industries, not just disrupting. Radically changing the makeup so before I dig into that. I want to get into digital bit. It's a little bit open source. So you have an open source project combined with what you guys do, so it sounds like you're what Red Hat was for Linux. You're for digital bits, is that? >> That's right so we are. So Fusechain is focused on building applications that are interoperable with that blockchain to support enterprises which is merchants, retailers, hotels so forth that would be working with the digital bits project. And so we feel that there is an opportunity to monetize that building let's say SAS type models around these applications and supporting and helping make digital events very successful. >> So it's interesting, I was observing when I was in New York last fall and I walked into a funds conversation with a bunch of guys. And people were trying to grop where the action was and I raised my hand and said, you can tell a good deal by the ones that are going to take down and incumbent industry, not just the player. You're taking a similar approach which I like about what your deal is. What is it about your approach and what is the target and how are you going to attack that? >> Sure, sure. First and foremost, really focused on blockchain and what was important for us characteristics wise and we felt that it needed to rapid transaction in terms of nature. Seconds as opposed to blocks, let's say every 10 minutes like a bitcoin for example. Because we are focused ultimately let's say on the consumer space. So we first and foremost on how our approach to developing this protocol and supporting the digital bits project. From there it was what industry did we feel would be best suited for this and this is how we gravitate into the loyalty industry. There is already a learned behavior in loyalty. People look at points as let's say a form of currency. They know how to go join one earn and what have you. It's like human mining, if you will and so we wanted to fit let's blockchain technology loyalty as opposed to fitting loyalty into blockchain. The other thing that I liked in terms of us going in this direction was really looking at. There was a lot of different ICOs, blockchain projects out there and so forth. We're the first to market with this. We're the first to market with that, but what's the incumbent doing in corporate America? Let's say, they're probably sitting and waiting and there's nothing preventing them copycatting and doing the same when there's enough of an established market. What I liked about loyalty more specifically the coalition models. We didn't feel that with a decentralized model. Putting into the market a decentralized model that they could replicate that the same way, It's like if you look at Netflix and what they did to Blockbuster. Blockbuster could not pivot quite the same way. We feel that loyalty dot one, specifically the coalition programs, will have a challenges in adopting blockchain in a similar manner. And so we feel that for that reason what we're up to here with this plain venture it's going to be highly disruptive. >> Let's get to the business model after we talk a little bit about the actual tech and the products. So you have digit bits and I notice you guys have a trade mark on that going on. But it's going to be open source. So what is digital bits? Is that the coin? Is it a utility token? How does it work? What are you actually doing? >> So digital bits is the name of the open source project. It's the name of the blockchain protocol. It will be the name of the cryptocurrency, so all the name of that cryptocurrency to that blockchain once it's put in circulation. And the project itself, we will ultimately see that spun into a foundation so it's the name of all of the above in terms of what digital bits is. Fusechain is a contributor to that project and we obviously like what it stands for. We're building parallel management platforms and so forth. Others are free to do this as well and have begun to do so. That will help make that project successful. >> So in other words, it creates a code from digital bits and apply it but you're going to be a token in the project. >> Yeah, if you think of, use Red Hat as an example. So there was open source project out there, various Linux type projects back in the day and big enterprises wanted to take advantage of that. But who was going to support them doing that? So Red Hat obviously established a very successful market in doing that so in a similar manner. We want to support digital bits in a very big way. We're building applications that businesses are going to need so they don't have to go build them themselves, and it will bring those markets. >> Who are you targeting? You're targeting existing businesses that have loyalty. You're trying to take that business away from them. Isn't that new? What the-- >> So coalition loyalty industry is fairly well established. >> John: What does that mean coalition? >> Coalition is multi merchant so in the United States, a brand known as Punti, that happens to be owned by American Express, but you can go to Macy's earn Punti, ExxonMobil and so forth. Canada is very big market for this as well so you have air miles, major grocery chains. >> John: They're always expiring, I hate these programs. >> Well that's the other issue with them. So there's tremendous friction and frustration now with these programs that exists. We're looking to disrupt that as well and provide-- >> So how do they work with you? Give an example of the use case that (indistinct talking). >> Ultimately we feel that, from a coalition standpoint often times the merchant is paying a reoccurring fee to support that program. So let's say big grocery store or hotel or what have you and in order for the privilege of their customers to be able to earn let's say, while shopping online at their store or in that facility just for the privilege of their users to be able to earn, the merchant is having to pay the operator that program, before the consumer has done anything with those points and so it's a big cost to them and we basically just to quantify, it can be as much of an 80% savings verses what the merchant would have to pay the support. One dot to support this decentralized blockchain base solution. >> So you guys are a decentralized application or are you a decentralized platform or you an infrastructure protocol? How do you categorically define yourself? >> So digital bits is definitely an infrastructure protocol but focus specifically on loyalty rewards and so just to, it's really opened in that sense that various businesses can join and support this. In a number of different ways whether it's pre-existing products, platforms that they have. They want it to be inoperable or they simply want their users to be able to now earn this form of loyalty. And we have in the coming weeks, you'll see announcements from other brands, some let's say blue chipish and others up and coming early stage companies with doing loyalty in a different way, joining the digital bits project to take advantage of the tokenize economy. >> I like this Red Hat to Linux in metaphor because I think no one's actually seen that yet happen. I see a lot of (indistinct talking) happen certainly the (indistinct talking) a decentralized apps or de-apps as they are called is huge growth market. We see a big tsunami coming with de-apps, decentralized applications. So will I be writing decentralized apps on your platform infrastructure? Is that they're doing? How are they implementing in your mind the Fusechain and the digital bits? >> So I mean there's basic examples of the products in market already, let's say multi-coin wallets. If they wanted to list digital bits as another cryptocurrency that their app supports then they can support the project in that way. So there's a number of different ways that the developers are established. >> I can build my own wall. I could integrate it into a pre-existing coin wallet. So you're pretty flexible, you're agnostic on how to gets done. >> Exactly, exactly. And this is why ultimately digital bits will be spun into a foundation. >> It will establish some policies around this so it's not completely naked but some governance. >> It's always tricky, you got to be careful. >> Well, governance from the standpoint of I'm looking at it from the perspective of how merchants, the terms by which they would disseminate digital bits to their consumers. >> So some lightweight governance. Is it hardcore governance or lightweight? >> No, I would say lightweight. So it's making sure that there's no bad actors at least at the time of-- >> (indistinct talking) a non-profit apart of the Fusechain? >> No, no, non-profit. >> Okay, okay so let's get into some of your journey. I see entrepreneurial journeys are happening all the time. A lot of people are jumping into the ICO and our crypto blockchain as a start. A lot of my alpha friends are doing it. It's just like wow. This is a big trend. It's disruptive. >> Al: Oh highly. >> Where there's disruption, you're going to have entrepreneurs but also scammers. We'll get that in a second but talk about your journey. ICO, you got to get formed. Get a little form, it could be expensive. We've documented theCube with Goodwin, a law firm in the valley that's doing a lot of ICOs. It could be expensive. There's tax consequences so how are you looking as an entrepreneur? You have opportunity recognition, check. Now you got to put it together. Utility token, are you raising money, are you doing the ICO? Can you give us some details? >> So it's utility token. We are raising money Fusechain initially is focused on raising capital, let's call it the old fashioned way. So Fusechain itself is taking in equity investment not involving any cryptocurrency. >> So no token sales on that simply. >> Is to date but a digital bits itself will be partaking and raising capital for the project. >> With Fusechain's ICO or their own ICO? >> No, no, it will be the digital bits projects. >> So will the ICO go through Fusechain or will go through digital bits? >> It will go through digital bits. >> Okay so you got a utility so that involves a token sales. So you're going to do a private, that's equity for Fusechain and then a token sale for digital bits. >> Al: Correct. >> Okay, that's nice-- >> Call it the pre-presale in advance of it actually being widely disseminated. >> What is the utility of the platform because that's the how we test? >> Yeah, yeah so we're keeping it really simple to start. We feel that we'll be able to demonstrate other utilities with this project, but similar to other projects out there if you're familiar with Ripple and Stellar and so forth. Some basic utility, you need to have some of the coin to be able to send coin. And so we're keeping it relatively simple from that perspective. There's security benefits. >> So the utility you're going after at launch is token sharing. >> Correct. >> Okay, and the activity is loyalty based for the merchants? >> Yes, and consumers so ultimately, digital bits stands for all these sort things I've just mentioned integrated together in this decentralized model really focused on giving back to users. So first and foremost, users being consumers that use these programs and the merchants that have historically supported these types of programs. In addition to that, digital bits is also focused on giving back to society. More specifically aligning itself with charitable organization worldwide that the project itself will be able to give back to. >> You're the (indistinct talking) guy. Your last (indistinct talking) you successfully sold it and exit pairing and networking. One big global network now. So I want to get your perspectives on entrepreneurs and how you've been traveling. We tried to get you last week here on theCube to talk about you're project and getting out there now but you've seen a lot of the events you're out in the field, you're own in the trenches. What's the landscape like in crypto and blockchain? Can you offer any observations? Good, bad and ugly, what's it take? >> I was for example recently last week I attended the North American Bitcoin Blockchain conference down in Miami, nearly 5000 people. Tremendous buzz, great pedigree among speakers. Both domestic speakers worldwide and people I would say from all walks of life. A lot of people are interested in either in the space or very interested in the space and I don't have the numbers in terms what the attendance was last year at that conference. But I wouldn't be surprised if it's 10x-- >> Are these new in tech? Are they tech gurus? What's the makeup and profile of folks in here? >> Overstock.com CEO. One of the keynote speakers of this and obviously a very well established company heavy in blockchain with their subsidiary t0 as well as some of the up and comers. Great pedigree, more specifically associated with the blockchain space but really supporting a lot of these events and being great evangelists for all things blockchain. >> So I get your perspective again. You see many ways of innovation, we're talking before we came on camera. I've been saying and when we talk privately in the valley here and in other places that this is like a dot com bubble, but it's accelerated. Everyone's getting their surf boards and jumping on those big waves. Some think there will be a crash. I think they'll be a probably a reset. There's just too much action happening and again the dot com bubble. Everything actually happens. >> Al: Yeah. >> So a little anecdote there but the point is there's some scammers. >> Al: Yes. >> There's some bubble activity. How are you sorting through that noise? What should people look through? Because when people are like, "Well I'm skeptical. "You're riding a hype wave right now. "What's the real deal?" >> The reality is with anything super exciting, there's always scammers. You have to take traditional stocks. There's always the penny stock scammers let's say and so this is not necessarily something exclusive to blockchain tokens or what have you. We see this in the traditional capital market systems and equities that are out there today. I'd say that this is very much mid 90s internet in terms of equivalent. The benefit of blockchain is that the internet exists so social network and Facebook. The ability to get news out there, widely disseminated, The internet existed. That infrastructure is helping to support the rapid growth trend that we're seeing with blockchain. So I would say that it is a bigger phenomenon than the internet was in the 90, by virtue the internet now existing. >> I got to ask you so one of the things I always is that there's no value being created. It's really a mirage right? So this thing about blockchain is there's a lot of value creation opportunities. As an entrepreneur, you get to see that and certainly see it from the Fusechain and digital bits. If someone said to, "Al, this thing is a bunch of hype. "Where's the value?" Where's the value? Why is crypto and blockchain attracting all these entrepreneurs? Why is it so intoxicating? Why is it attracting all walks of life? What's the value creation opportunity? >> Put cryptocurrencies aside for a moment and just focus on blockchain as a technology and really what it stands for. It is truly revolutionary. This is something with capability to have distributed ledgers solving the double spend issue. All of these things that historically could not be done with the internet or other forms of technology. And so it's very powerful in terms of its applications in areas of let's say even supply chain and how businesses can have this trusted collaborative platform or technology where you don't have to trust any centralized corporation, other institution or what have you, and it just works. So that is the technology itself is highly powerful and it's already evident that it's touching a number of different industries. So outside of the cryptocurrencies, let's say craze. Blockchain is definitely here. It's here to stay and it's just going to continue-- >> That's a fundamental infrastructure shift. >> Absolutely. >> Alright, so let me give you the little snarky comment that get on Facebook all the time. "Ah John crypto, this blockchain. "Have you seen a distributed database before, lol?" That's some snarky comments. So the naysayers will be like, "It's just a distributed database ledger." And then some people will be like, "I just don't see the business case. "Why do people actually need blockchain?" What's your take on those two points? >> I think that, that's a great way to look at it. Can you solve that problem with just using regular database? And probably often times the answer is yes, so blockchain shouldn't necessarily be used for everything, but there is certain things that historically, and again-- >> (indistinct talking) is one. >> Exactly, yeah. >> (indistinct talking) attracts. >> Absolutely, and so there's a number of industries where having it be blockchain based is definitely better than dealing with distributed databases. >> I've been commenting. I'm pro-blockchain as you know. Pretty bias, people know that. However what I say to folks is look, there's a dynamic going on here that's revolutionary at the infrastructure level. I think that's true. That will play out and then I think immutability and then the decentralized nature of apps. It will be a whole another genre of software development whether it's media (indistinct talking) to software. But ultimately it's these communities, if you look at in the media business. I was just at Sundance. There's new artist coming on that have their own audiences. >> Al: Right. >> So those are crushing the elites. So you have a revolution where the common person or group of people could get together in an unstructured way, a decentralized way to take on elite or huge industry incombantants or industries themselves. That's a phenomenon. That's kind of nuance. >> Al: Absolutely. >> It's real. >> It's absolutely real. Think of open source traditionally. You needed your employer to sponsor you. Hey if work for you, can I spend 10% of my time on a open source project? The open source project itself never really had a mechanism to provide support form of remuneration. Now by tokenising and so forth these native currencies an idea can provide a potential for reward and we're seeing that happen, and so it no different than any other great idea. 90 plus % of start ups don't necessarily make it. 90 plus % of blockchain ideas may not make it but the reality is, a community with a great idea can kick off a project on their own and stand the test of time. >> Well Red Hat became popular from Linux which was a second tier citizen in an open source. Now it's tier one also open source is running things so I got to ask you a final question on the business model. How are you guys planning on making money? Is it from support in the open source projects specifically, more services on the coin side. Is it managing the coins? Do you have visibility yet into that model? >> Yes, so I would say yes to what you just said. So Fusechain will create shareholder value in a few different ways. One, obviously being one of the first supporters to the digital bit project. We obviously want to see that project wildly successful, coin appreciation and the asset appreciation that potential could occur there will create shareholder value for Fusechain. In addition to that, Fusechain is building applications that will be SAS like in model. We'll be able to derive a reoccurring revenue. (indistinct talking) models but we'll derive reoccurring revenues. >> For the ecosystem of saving the digital bits actually it evolves. >> Right, merchants, you can go build softwares yourself or here's a subscription based platform that you can use and we'll provide support as well. >> Having fun? >> I'm having a blast. It's the 90s all over again. >> It the twinkle of the eye. I got to say, it's super intoxicating. I'll take hit of that blockchain in next segment with you. Appreciate it, it's really awesome. Blockchain and crypto, really amazing revolution. We're doing our part to unpack it, analyze it and also look at the good deals out there. This is SiliconANGLE theCube here in Palo Alto. I'm John Furrier. Special exclusive to you conversation with Fusechain coming out, talking about their project for the first time digital bits with Al Burgio, the founder and CEO. Thanks for watching. (uptempo orchestral music)

>> Announcer: From Denver, Colorado, it's theCUBE, covering Super Computing 17. Brought to you by Intel. (soft electronic music) Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're gettin' towards the end of the day here at Super Computing 2017 in Denver, Colorado. 12,000 people talkin' really about the outer limits of what you can do with compute power and lookin' out into the universe and black holes and all kinds of exciting stuff. We're kind of bringin' it back, right? We're all about democratization of technology for people to solve real problems. We're really excited to have our last guest of the day, bringin' the energy, Armughan Ahmad. He's SVP and GM, Hybrid Cloud and Ready Solutions for Dell EMC, and a many-time CUBE alumni. Armughan, great to see you. >> Yeah, good to see you, Jeff. So, first off, just impressions of the show. 12,000 people, we had no idea. We've never been to this show before. This is great. >> This is a show that has been around. If you know the history of the show, this was an IEEE engineering show, that actually turned into high-performance computing around research-based analytics and other things that came out of it. But, it's just grown. We're seeing now, yesterday the super computing top petaflops were released here. So, it's fascinating. You have some of the brightest minds in the world that actually come to this event. 12,000 of them. >> Yeah, and Dell EMC is here in force, so a lot of announcements, a lot of excitement. What are you guys excited about participating in this type of show? >> Yeah, Jeff, so when we come to an event like this, HBC-- We know that HBC is also evolved from your traditional HBC, which was around modeling and simulation, and how it started from engineering to then clusters. It's now evolving more towards machine learning, deep learning, and artificial intelligence. So, what we announced here-- Yesterday, our press release went out. It was really related to how our strategy of advancing HBC, but also democratizing HBC's working. So, on the advancing, on the HBC side, the top 500 super computing list came out. We're powering some of the top 500 of those. One big one is TAC, which is Texas Institute out of UT, University of Texas. They now have, I believe, the number 12 spot in the top 500 super computers in the world, running an 8.2 petaflops off computing. >> So, a lot of zeros. I have no idea what a petaflop is. >> It's very, very big. It's very big. It's available for machine learning, but also eventually going to be available for deep learning. But, more importantly, we're also moving towards democratizing HBC because we feel that democratizing is also very important, where HBC should not only be for the research and the academia, but it should also be focused towards the manufacturing customers, the financial customers, our commercial customers, so that they can actually take the complexity of HBC out, and that's where our-- We call it our HBC 2.0 strategy, off learning from the advancements that we continue to drive, to then also democratizing it for our customers. >> It's interesting, I think, back to the old days of Intel microprocessors getting better and better and better, and you had Spark and you had Silicon Graphics, and these things that were way better. This huge differentiation. But, the Intel I32 just kept pluggin' along and it really begs the question, where is the distinction now? You have huge clusters of computers you can put together with virtualization. Where is the difference between just a really big cluster and HBC and super computing? >> So, I think, if you look at HBC, HBC is also evolving, so let's look at the customer view, right? So, the other part of our announcement here was artificial intelligence, which is really, what is artificial intelligence? It's, if you look at a customer retailer, a retailer has-- They start with data, for example. You buy beer and chips at J's Retailer, for example. You come in and do that, you usually used to run a SEQUEL database or you used to run a RDBMS database, and then that would basically tell you, these are the people who can purchase from me. You know their purchase history. But, then you evolved into BI, and then if that data got really, very large, you then had an HBC cluster, would which basically analyze a lot of that data for you, and show you trends and things. That would then tell you, you know what, these are my customers, this is how many times they are frequent. But, now it's moving more towards machine learning and deep learning as well. So, as the data gets larger and larger, we're seeing datas becoming larger, not just by social media, but your traditional computational frameworks, your traditional applications and others. We're finding that data is also growing at the edge, so by 2020, about 20 billion devices are going to wake up at the edge and start generating data. So, now, Internet data is going to look very small over the next three, four years, as the edge data comes up. So, you actually need to now start thinking of machine learning and deep learning a lot more. So, you asked the question, how do you see that evolving? So, you see an RDBMS traditional SQL evolving to BI. BI then evolves into either an HBC or hadoop. Then, from HBC and hadoop, what do you do next? What you do next is you start to now feed predictive analytics into machine learning kind of solutions, and then once those predictive analytics are there, then you really, truly start thinking about the full deep learning frameworks. >> Right, well and clearly like the data in motion. I think it's funny, we used to make decisions on a sample of data in the past. Now, we have the opportunity to take all the data in real time and make those decisions with Kafka and Spark and Flink and all these crazy systems that are comin' to play. Makes Hadoop look ancient, tired, and yesterday, right? But, it's still valid, right? >> A lot of customers are still paying. Customers are using it, and that's where we feel we need to simplify the complex for our customers. That's why we announced our Machine Learning Ready Bundle and our Deep Learning Ready Bundle. We announced it with Intel and Nvidia together, because we feel like our customers either go to the GPU route, which is your accelerator's route. We announced-- You were talking to Ravi, from our server team, earlier, where he talked about the C4140, which has the quad GPU power, and it's perfect for deep learning. But, with Intel, we've also worked on the same, where we worked on the AI software with Intel. Why are we doing all of this? We're saying that if you thought that RDBMS was difficult, and if you thought that building a hadoop cluster or HBC was a little challenging and time consuming, as the customers move to machine learning and deep learning, you now have to think about the whole stack. So, let me explain the stack to you. You think of a compute storage and network stack, then you think of-- The whole eternity. Yeah, that's right, the whole eternity of our data center. Then you talk about our-- These frameworks, like Theano, Caffe, TensorFlow, right? These are new frameworks. They are machine learning and deep learning frameworks. They're open source and others. Then you go to libraries. Then you go to accelerators, which accelerators you choose, then you go to your operating systems. Now, you haven't even talked about your use case. Retail use case or genomic sequencing use case. All you're trying to do is now figure out TensorFlow works with this accelerator or does not work with this accelerator. Or, does Caffe and Theano work with this operating system or not? And, that is a complexity that is way more complex. So, that's where we felt that we really needed to launch these new solutions, and we prelaunched them here at Super Computing, because we feel the evolution of HBC towards AI is happening. We're going to start shipping these Ready Bundles for machine learning and deep learning in first half of 2018. >> So, that's what the Ready Solutions are? You're basically putting the solution together for the client, then they can start-- You work together to build the application to fix whatever it is they're trying to do. >> That's exactly it. But, not just fix it. It's an outcome. So, I'm going to go back to the retailer. So, if you are the CEO of the biggest retailer and you are saying, hey, I just don't want to know who buys from me, I want to now do predictive analytics, which is who buys chips and beer, but who can I sell more things to, right? So, you now start thinking about demographic data. You start thinking about payroll data and other datas that surround-- You start feeding that data into it, so your machine now starts to learn a lot more of those frameworks, and then can actually give you predictive analytics. But, imagine a day where you actually-- The machine or the deep learning AI actually tells you that it's not just who you want to sell chips and beer to, it's who's going to buy the 4k TV? You're makin' a lot of presumptions. Well, there you go, and the 4k-- But, I'm glad you're doin' the 4k TV. So, that's important, right? That is where our customers need to understand how predictive analytics are going to move towards cognitive analytics. So, this is complex but we're trying to make that complex simple with these Ready Solutions from machine learning and deep learning. >> So, I want to just get your take on-- You've kind of talked about these three things a couple times, how you delineate between AI, machine learning, and deep learning. >> So, as I said, there is an evolution. I don't think a customer can achieve artificial intelligence unless they go through the whole crawl walk around space. There's no shortcuts there, right? What do you do? So, if you think about, Mastercard is a great customer of ours. They do an incredible amount of transactions per day, (laughs) as you can think, right? In millions. They want to do facial recognitions at kiosks, or they're looking at different policies based on your buying behavior-- That, hey, Jeff doesn't buy $20,000 Rolexes every year. Maybe once every week, you know, (laughs) it just depends how your mood is. I was in the Emirates. Exactly, you were in Dubai (laughs). Then, you think about his credit card is being used where? And, based on your behaviors that's important. Now, think about, even for Mastercard, they have traditional RDBMS databases. They went to BI. They have high-performance computing clusters. Then, they developed the hadoop cluster. So, what we did with them, we said okay. All that is good. That data that has been generated for you through customers and through internal IT organizations, those things are all very important. But, at the same time, now you need to start going through this data and start analyzing this data for predictive analytics. So, they had 1.2 million policies, for example, that they had to crunch. Now, think about 1.2 million policies that they had to say-- In which they had to take decisions on. That they had to take decisions on. One of the policies could be, hey, does Jeff go to Dubai to buy a Rolex or not? Or, does Jeff do these other patterns, or is Armughan taking his card and having a field day with it? So, those are policies that they feed into machine learning frameworks, and then machine learning actually gives you patterns that they can now see what your behavior is. Then, based on that, eventually deep learning is when they move to next. Deep learning now not only you actually talk about your behavior patterns on the credit card, but your entire other life data starts to-- Starts to also come into that. Then, now, you're actually talking about something before, that's for catching a fraud, you can actually be a lot more predictive about it and cognitive about it. So, that's where we feel that our Ready Solutions around machine learning and deep learning are really geared towards, so taking HBC to then democratizing it, advancing it, and then now helping our customers move towards machine learning and deep learning, 'cause these buzzwords of AIs are out there. If you're a financial institution and you're trying to figure out, who is that customer who's going to buy the next mortgage from you? Or, who are you going to lend to next? You want the machine and others to tell you this, not to take over your life, but to actually help you make these decisions so that your bottom line can go up along with your top line. Revenue and margins are important to every customer. >> It's amazing on the credit card example, because people get so pissed if there's a false positive. With the amount of effort that they've put into keep you from making fraudulent transactions, and if your credit card ever gets denied, people go bananas, right? The behavior just is amazing. But, I want to ask you-- We're comin' to the end of 2017, which is hard to believe. Things are rolling at Dell EMC. Michael Dell, ever since he took that thing private, you could see the sparkle in his eye. We got him on a CUBE interview a few years back. A year from now, 2018. What are we going to talk about? What are your top priorities for 2018? >> So, number one, Michael continues to talk about that our vision is advancing human progress through technology, right? That's our vision. We want to get there. But, at the same time we know that we have to drive IT transformation, we have to drive workforce transformation, we have to drive digital transformation, and we have to drive security transformation. All those things are important because lots of customers-- I mean, Jeff, do you know like 75% of the S&P 500 companies will not exist by 2027 because they're either not going to be able to make that shift from Blockbuster to Netflix, or Uber taxi-- It's happened to our friends at GE over the last little while. >> You can think about any customer-- That's what Michael did. Michael actually disrupted Dell with Dell technologies and the acquisition of EMC and Pivotal and VMWare. In a year from now, our strategy is really about edge to core to the cloud. We think the world is going to be all three, because the rise of 20 billion devices at the edge is going to require new computational frameworks. But, at the same time, people are going to bring them into the core, and then cloud will still exist. But, a lot of times-- Let me ask you, if you were driving an autonomous vehicle, do you want that data-- I'm an Edge guy. I know where you're going with this. It's not going to go, right? You want it at the edge, because data gravity is important. That's where we're going, so it's going to be huge. We feel data gravity is going to be big. We think core is going to be big. We think cloud's going to be big. And we really want to play in all three of those areas. >> That's when the speed of light is just too damn slow, in the car example. You don't want to send it to the data center and back. You don't want to send it to the data center, you want those decisions to be made at the edge. Your manufacturing floor needs to make the decision at the edge as well. You don't want a lot of that data going back to the cloud. All right, Armughan, thanks for bringing the energy to wrap up our day, and it's great to see you as always. Always good to see you guys, thank you. >> All right, this is Armughan, I'm Jeff Frick. You're watching theCUBE from Super Computing Summit 2017. Thanks for watching. We'll see you next time. (soft electronic music)

>> Announcer: Live from Las Vegas. It's theCUBE! Covering Dell EMC World 2017. Brought to you by Dell EMC. >> Welcome back everyone to theCUBE's coverage of Dell EMC World. I'm your host Rebecca Knight. Along with cohost Keith Townsend. We are joined by Todd Pavone, he's the COO of Dell AMC. Thanks so much for joining. >> Well all my kids call me dad, whatever. >> Rebecca: We can call you anything you want. >> Dad's good, yeah. >> Daddio alright. >> So Todd, the theme of this conference is about providing companies with the tools that they need to realize their digital futures. Lay it all out for me. What do you see as sort of the biggest problems that you're trying to solve. >> Sure, that's a good question. If you're a CIO today, I think there's lots of them in this show these past few days. They have lots of challenges, right. If you're CIO today, your budgets are flat or decreasing. Your users are asking to do more faster. The evolution of change is at a crazy pace. You look at your operating budget, the mast majority is spent on maintenance. So as a CIO you have to do something different. You have to transform. If you think about that, IT has changed so much in the last 10 years. IT is actually the differentiator if the business will succeed or fail. Years ago, I've been doing this a long time, years ago it was just a support function. It is now the differentiation if you'll succeed or fail. So if you think about that scenario of a CIO, they have to change. They have to transform. They have to modernize their data center. So everything we've been doing for the last several years is helping the CIOs and data centers transform. Transform so they can simplify the complex. So they can deliver faster. They can have better service levels with the users. So their staff can actually work on strategic initiatives, not on the things that just are keeping the lights on. So they can help their business transform and differentiate. And that's what's it's all about. Is helping them transform. >> So what are you seeing out there right now that is making you confident about the landscape. >> So for us, in CPSD, the BU we've been in, we started this about seven or eight years ago. What we see is results. It's all about delivering results. We're seeing customers improve their time to delivery by 4x. Customers cutting their total cost of ownership by 50%. Customers able to provision new services to the end users at a 5x rate. Measurable, tangible results. The metric I care most about in our business is repeat customers. About 70% of our customers are repeat. Meaning they're deploying the technology and solution and they're seeing the benefits. They're saying this is how we want to run data center of the future. What's really cool is, we're helping them change their businesses and be more successful long-term. That's the best part of what we're doing. >> So let's talk about overall market. Where is Dell in this market? How big is the market? And where's Dell in this market? >> Dell technology, you're talking about hundreds of billions dollars market. It's an enormous market. From a Dell technology perspective, it's all about how do we help transform the data center. How do we provide the essential infrastructure for the data center. If you start to peel that back and you look at the market that our business unit is in, Converged Platforms and Solutions Division, we're focused on the hybrid cloud solutions engineering systems all the way down to our Blueprints solutions. You're talking about a 20, 30 billion dollar market, depending on how you look at it. It's big and growing. Fortunately, our space is one of the fastest growing markets in the industry. Customers are deploying converge and hyper-converge at a rapid pace. And thankfully we're the ones helping drive that that change. >> So what are the big value props coming from a technology perspective. You talked about hybrid cloud. You talked about the actual systems, what are the value props for Dell's converge systems versus competitors. >> So if you think about the top x we talk about this build to buy continuum. So what we want to be able to do is have a continuum of offerings, depending on where the customer's maturity is, they can leverage wherever we are in their transformation. At the tip of the pyramid is our hybrid cloud solution. So this where companies may want to deploy a turn key hybrid cloud. From application all the way to infrastructure and within a very short period of time have a fully enabled running cloud that they could offer to their end user customers. Value for that is time to market. We have customers deploying a full hybrid cloud in days. If they had to build themselves, it would take months and many months. So speed is a huge part of that value proposition. Obviously leveraging of VMware technology in that solution is key to what we're doing. Leveraging Pivotal for our native hybrid cloud solutions is very differentiated. Those are two technologies that are in the Dell technology portfolios, VMware and Pivotal that we can take advantage of. In the engineered systems landscape, I hit on this before, but it's all about speed to deliver. We know if customers were to build things out themselves, it would take them 140 to 150 days. We know we can do it in 45 days. So think about that, it's a three to four times x improvement in delivery. We know it requires less people to manage. We know we can save 50%-60% total cost of ownership by deploying these engineer systems versus managing themselves. Those are big numbers. Those are numbers where you can actually take those dollars and reinvest in other things. It comes back to what I said upfront, it's all about solving those pain-points for the customers. Simplifying the complex, allowing them to go work on that are really strategic to their business. I used an example yesterday of Blockbuster. My three boys and I would go down to Blockbuster every Friday night, we'll rent movies. Blockbuster is not here anymore. >> Rebecca: Nope. >> What happened? I can tell you, their IT organization was supporting their point of sale systems in their stores. They weren't thinking of new ways to distribute content. Why? Because they had no time. We want companies to be able to have the time to work on strategic things that are important to them. We want to enable them to do that. >> So that's a great example of being short sightedness. And the dangers of being short sightedness at time of profound transformational change. What is your call to arms to customers, what's your advice? >> Customers have to, it's hard to change. It's easy- >> Rebecca: And you resist it. It is. >> You have people that have been doing things for a long time, it's hard to change. It's not just about technology change, it is about changing process, changing the way people work. You have to have the intestinal fortitude to go do it. People when they see change, they kind of want to run from it. You have to embrace it, adopt it. Because if you don't, you may not be positioned in the long-term. First thing, for us, we need change agents. We need customers to have change agents that are willing to put push back, willing to try to do something different. Willing to innovate, I'm willing to break some glass. And when they do that, at the end of the day, we know they're going to be better off for it. But again, it's hard to do. That's probably the first thing we ask them, be willing to accept some major changes in what they do and how they do it. >> So this seems like this would be something that's contagious. Once a customer embraces that change, talk us through that next level of conversation and the future conversation. >> Typically there needs to be an event. Something to trigger them to go change. Could be data center consolidation, it could be a new application rollout. It could be a strategic initiative by the executive team. So most of our customers will start with maybe one or two applications. And they'll say, let's go see how this works. Let's go test how this works. Especially if they're big. If they're a large enterprise, they're not going to do a global change for everything. They're going to say, let's start with a subset. Once they see the subset work, then they start rolling more and more applications into this new way of running their data center. When we talk about with our customers, I like to use autonomous driving as an example. To me, we see a data center someday where basically, things will happen autonomically. You'll set a business policy, workload will dynamically base upon that policy. We're requiring humans not to get involved. It's kind of the same thing, back to my analogy of Tesla. You get into Tesla, the car actually starts to drive itself. How is it doing that? It's doing real-time data analytics, it's understanding all its environmentals, it's making decisions. Why can't the data center get to the same place? Because if you can do that, think all the savings you can now reinvest in the things that are real important to the business. So we try to set a strategy and vision that customers understand and appreciate. And then we try to give them the building blocks of how they're going to get there. >> Just as we'll remember the days when we used to drive to the supermarket, we'll remember the days when we had to run the data center. >> Exactly. That's the hope. That's the hope and dreams of what we're trying to do. >> Next year's conference. >> Next year's conference. >> I'm all in. I'm all in. >> We got more. We got more. Thanks so much Todd. >> Todd: Thank you guys. >> I appreciate it. >> This is great. >> Great to have you on the show. >> I'm Rebecca Knight. For Keith Townsend. We will be back with more after this. (Lively techno music)

>> Narrator: Live from Boston, Massachusetts, it's the Cube. Covering Red Hat Summit 2017, brought to you by Red Hat. (techno music) >> Welcome back to Boston, everybody. And welcome back to Red Hat Summit. This is the Cube, the leader in live tech coverage. My name is Dave Vellante, and I'm here with my co-host, Stu Miniman. Stu, we were saying this is your 100th Red Hat Summit, so congratulations on reaching that milestone. Joe Dickman is here. He's the senior vice president of Vizuri. Cool name, love it. And Michael Quintero, or Quintero if you prefer, of LogistiCare. He's an enterprise solutions architect. Gentlemen, welcome to the Cube. >> Thank you. It's a pleasure to be here. >> So Vizuri. Love the name. It strikes a visualization. It's (mumbles) trendy. Tell us about Vizuri, and tell us about your relationship with LogistiCare, and we'll get into it. >> Vizuri is the private division of a company called AEM Corporation. We created the brand to serve the commercial market for research and development. We became partners with JBoss before Red Hat's acquisition, so we jumped into open source in like 2003. And since then, we've built a business around open source technologies, and market leading technologies that bring value. We found LogistiCare because they solicited us for some work to help them transform their organization. And it's worked out well. I mean, Michael and I have been working together for about 18 months. >> So, tell us a little bit about LogistiCare. >> So LogistiCare is the world's largest provider of non-emergency medical transportation. So, we service the health market around people have benefits. The insurance companies don't provide transportation, and the members come to us and we broker the transportation for them. Been in business for quite some time. Do about 70 million trips a year, a little bit more. And we have roughly 80% of that market. And we just want to stay on top of, and be recognized as the world leader in that capability with the best services and the care for our members. >> So JBoss of course was like the second pillar for Red Hat after Red Hat (mumbles) Rob Bearden, who was a CEO at the time, and Cube alum and friend. But so, how did you utilize that capability, the sort of whole middleware, and how does that affect your digital transformation? And where did you guys all fit together? >> So, well digital transformation is a business strategy, not a technology. So, we looked at our need to be more flexible, and dynamic, and innovate. Our legacy, our what we call classic internally, software stack is limiting. It's not service oriented. It's not extensible. It's a compiled, executable, distributed -- serves the business very well. In fact, we're still using it today in some aspects. We haven't fully replaced it. But it's long in the tooth, and it's difficult for us to reach that new business requirement and test and deliver it scale. So, I joined the company to help modernize that architecture. Very quickly recognized that in order to get to scale, and loosely coupling, and massive customization, that microservices was a good solution for us. And when we surveyed the market for a partner that could help take us there, software wise, Red Hat has the most complete stack. They offer everything we need to do, and then they have the things we think we're going to do in the future. So, we looked around for somebody who could help us get to the Red Hat, enable to that, with Docker, and get to an auto-scaling kind of solution so we have infrastructure on demand. And we found Vizuri as a partner. They were able to help us enable the technology and teach us how to do things that we weren't presently doing. Because we didn't have any kind of scale solution in-house, it was just put more web servers out there. >> We started small, it started with a Business Process Management System. If you think about all the logistics that are necessary for coordinating medical transport, "I'm a dialysis patient. I'm somebody that is home-bound. I need to get to a physician appointment." We took that domain knowledge, that's part of one of the pillars of digital transformation. It's infrastructure, it's integration, and it's knowledge management. We started with knowledge management. Think about all the complex business rules for manage care organizations, reimbursement, right? Which is what LogistiCare does. Quickly after we solved that problem, we looked at integration, and we said, "Well now we have all these trading partners." So we guided LogistiCare into their next purchase which was Fuse. So now we had an API strategy for publicly linking them to other consumer providers, because they are a logistics organization for reimbursement. And as Michael said, we started building data centers. Or LogistiCare did. But guess what? Containers and OpenShift came in and we started provisioning our development environments to Amazon Web Services. And when they saw the cost-savings, they abandoned building out on-prem data centers, and went Cloud-native. >> So there's also a revenue drive, or component, as well, right? >> It is. It is. It's an OpEx (mumbles) and the CapEx cost-savings. >> Let's unpack both of those. >> Joe: Sure. >> Where do you want to start? Cost or the telephone numbers? (laughs) >> So, we're mostly a call center based company in history. Right? We have 20-something call centers around the country. We service most of the U.S. And we have a variety of contracts with medical care providers, like Aetna, and Wellpoint, and Blue Cross, and those type people. And then the managed care organizations come in. So, we look to reduce our OpEx by diminishing the number and the interfaces that we have with our call centers. People don't have to call in to the call centers to do business with us. You know, something like one-minute reduction in call-time is about a six or seven million dollar a year benefit for us. And there's a lot of things that people can do for themselves. I mean, you can call in and cancel a trip that they've had scheduled. We figured that about 30% of the cancellation rate, if we could get that done through a service interface, through an IVR, where you can come in and say "I'm not going to go." and cancel it. That's a five or six million dollar savings for us right there. Just in 30%. >> Michael, I'm curious. Was there any hesitancy inside to say, "Okay. I'm going to kill data centers, going to go to a public Cloud." You know, how did that transition go? And anything, you know, kind of the good, the bad, and the ugly that you could share. >> So, well, we're a healthcare company. HIPA and HITRUST certified coming. And there's a certain amount of fear on Cloud migration. So we had to demonstrate the knowledge, skills, and abilities around getting secure, scalable solutions out to the Cloud. And this is our core application. If we don't do this well, we could become Blockbuster and go away. Right? So we don't want that. So, we had Vizuri come to the table and help us understand just how secure we can be, how OpenShift is helping us make sure our information is never violated. There's great integrity in it. And then we did prototyping, and we actually evaluated it, and we have third parties that come in and take a look at our solution and say, "Can I penetrate that? Can I get into your information?" So, and, we also are subject to audit, not only by the federal government, but by all of our payer partners. So we have to be above the line in every criteria, and we think that we are. >> The other thing that you mention was, when we talk about OpEx, right? That's human capital. He talked about the minute per time on a call. We also reduce tribal knowledge. Think about all these new managed care organizations in health care. Is it the call center representative, is it our responsibility to train them on this car, and this company requires a car service, this company requires an ambulance. That knowledge, if we could eliminate that and put that in the middle tier. Now what we do is we have given them a business scale. Now they have a business strategy for taking on new managed health care organizations. Do you have different compliance rules? Do you have different knowledge? It is no longer us having to go back out to those 20 call centers and re-train everybody, because you never know where the consumers are coming from. So, what they do is they answer the phone, they put their information into the system, and the system makes the deterministic call as to what car service, when, and how it's reimbursed. >> So, you say you automated essentially that tribal knowledge. >> Joe: We did. >> Eliminated it. >> And we reduced it so it not only reduced the calls per time frame, but it sped up our time of getting a call center agent from three weeks of training down to basically one. >> Yes, and we have the ability now to support all of our contracts from any call center. So if there's disaster recovery models, or, you know, Phoenix for instance is one of our larger call centers and they get heavy downpours of rain there. There are times when people can't get to work, or they have outages. We can't afford for that function to be offline. So those skills are very easily moved to another call center to support the members that would call in there. Just route the calls. And there's no local knowledge about, you know, my contract in Arizona does a certain thing, or in the Southwest, so it's very simple to support our population from any call center. That gives us the benefit of providing very high quality service, 'cause people when they call in, they expect us to service them. >> Joe, I want to follow up. We were talking about kind of, you know, hesitancy, healthcare tends to be a little bit conservative. I hear things like microservices, and containers. You know, these are still relatively new things. Is (mumbles) -- sorry, OpenShift the solution that allows you to deliver that with confidence to your customers? >> Yes. OpenShift. (laughs) >> Yeah, sorry about that. (laughs) >> No worries. (laughs) OpenShift does. What happens is the Docker container format enables us to pre-configure those servers and those workloads, and we talked about microservices. We wanted to reduce the business decisions or the integrations into the smallest component. What we also wanted to do was provide some taxonomy with them. These are for billing, these are for scheduling, these are for a different aspect of the business. By that, we can change, and we can change often. >> Mhm. >> How long did it take before if we wanted to make a change to some of the infrastructure? >> So. >> Weeks? Months? >> Well, even longer. I mean infrastructure is hard to acquire. And you only talk about CapEx expense. It's very easy, I mean there's a refresh cycle for equipment that you get. So even when you have it, you have to pay attention to maintenance and keeping that thing going forward. As you add scale to your business, you got to go acquire more storage. And it's not a dynamic thing. You have to plan -- the planning cycle is very difficult. We moved to the Cloud. Now we have infrastructure on demand. There's a myriad of choices of platforms and solutions that we can apply to our business model. Things we hadn't even thought of before. We're actually looking now at potentially moving our call centers away from our in-house standard, and moving to an Amazon provided call center solution. Because it can scale. And we can consolidate. And we can provide service from anywhere in the world. That's a big benefit to us. >> It is. So call center as a service, essentially. >> Michael: Yes. >> Is something you're evaluating. >> Think about how big they are. 80 million rides, right. What they didn't want to do is be disintermediated by the newcomers. Right? The Uber's, the Lyft's. They had a large footprint. So, he used the word Blockbuster before, and that's what they use a lot internally. >> Dave: There's one left, in Alaska, I heard. (laughs) >> Who remembers Blockbuster? And then they remember how Blockbuster was no longer in business. So what they wanted to do is to ensure that -- they agilely transformed not only the software engineering discipline, but their firm beliefs. So, everybody from business analysis through implementation has this new agile approach. And one of the features that we developed, we used to send people home after four hours of dialysis in taxi cabs. So, an executive, or team, at LogistiCare said, "We need dependency. We need certified drivers." They actually entered into a business relationship with Lyft. And you want to talk about an agile enterprise? We developed a custom interface into Lyft with a scheduling service that never existed, within five weeks. >> Michael: That's right. >> We would never have been able to do that. And we moved our first ride after five weeks, and since then, we're currently up to about five or six thousand. But it's going to scale to thousands. And the goal is to, again, as Michael said, let people interface with LogistiCare by their device of choice. If we don't have to have people call in to cancel rides, or call in to schedule, then the business scales, and it scales without human capital. >> And the enablers there, (mumbles) we always talk about it, people, process, and technology. So the technology behind that was, what, you're living this API economy that everybody talks about. >> Michael And Joe: We are. >> Joe: That is exactly what we did. >> And then you've got underneath that, OpenShift, what else is sort of there that you're leveraging? >> BPMS, BRMS. So, Business Process Management System. Business Rules Management System. JBoss fused for an integration strategy and Camel Routes. And then Openshift, and then we do Ansible for doing server provisioning. >> And I have to ask you about the security question again. Stu was (mumbles) poking at it before. We've heard from a lot of practitioners that the security in the Cloud is just fine, it is great actually. The challenge is, it doesn't necessarily exactly map the edicts of our organization. So, is that, did you find that? And did you have to maybe change the way in which you plugged into AWS, or was it just sort of out of the box for you? >> So, you have to understand the shared responsibility model when you move to the Cloud, right? I mean they're very good at the security in the Cloud, or of the Cloud, and you have to be good at the security in the Cloud. You can choose bad technology at Amazon and be insecure. But they have a published, HIPA standard, that if you use these technologies, then you can be HIPA certified. We applied our HITRUST certification standards to our choices. We're making very solid -- and this isn't willy nilly. I mean I've been in a HIPA solution for 20 years. So it's not like I don't know what is required, and what the auditors are going to ask us. So, but I do want to redress one point that we can't go past. Is that (mumbles) Our customers are getting better service from all this we're doing. >> Joe: I agree. >> When somebody calls us and says, "I'm ready to go home from the doctor." and they didn't know what time they were going to go home when they scheduled their ride to the doctor, we can get somebody there in 10 minutes now to come and get them and take them home. >> Dave: Wow. >> That's a great satisfier. Rather than having to wait 90 minutes for us to find somebody that can go pick them up. That world has changed, right? And that's a great customer satisfier and that is why they're going to love continuing to do business with us. >> Great business outcome from something that you probably couldn't have done, you know, five years ago? Even maybe two years ago. >> They're a social caring organization. One of the largest rides that they do is for kidney dialysis. And those people, I mean, I've never had it, but somebody sitting there after four hours of dialysis, the last thing you want to do is wait 90 minutes for a cab. You want to go home. You also want to have an authoritative source that the drivers are credentialed drivers. And that's something that we're working on so that not only do these older generations, right? And think about the baby boomers, which I'm actually part of. >> Michael: Me too. (laughs) >> The age population is growing. So the need for these types of services is growing too. And we become accustomed and we get set in our ways. And people might be fearful. Any taxi showing up, versus now, a Lyft shows up, you know who the driver is. You see the car, you see that. There's a high degree of confidence that LogistiCare has the best interests of their constituents. So they manage that type of business. So it's not just technology, it really is a caring and methodical organization. >> But we have the ability to follow patterns that are already established. We look at how Netflix handles their widely distributed kinds of interface devices. You know, how do they figure out what kind of data-stream to send back to what he's got in his hand versus what I have. We're following the same kind of model, and we're using the technology platform to our best advantage to make sure that we're talking to someone who's got a flip-phone differently than we are talking to someone who's got a (mumbles) Plus, right? (Dave laughs) Because the payload can't be the same, but the backend services don't need to know that. We built a solution here that can examine the request and return the right data-stream. So, "Where's my ride?" Might be "Just around the corner." or it might be a map with a breadcrumb trail and a picture of the driver and all of that. Like you get with a Lyft or an Uber. So, you know, we're building it. >> Great case study, gentlemen. Thanks very much for coming to the Cube and sharing it. >> Well, thank you very much for having, we enjoyed the time. >> Alright, keep it right there everybody. We'll be right back with our next guests. This is the Cube. We're live from Red Hat Summit in Boston. Be right back. (electronic music)

>> Announcer: Live from Las Vegas, it's theCUBE, covering Oracle Modern Customer Experience 2017, brought to you by Oracle. (techno music) >> Okay, welcome back, we're live here at the Mandalay Bay, this is theCUBE's coverage of Oracle's Modern Customer Experience event, I'm John Furrier with SiliconANGLE and my co-host Peter Burris, Chief Researcher at Wikibon.com, and our next guest is Nick Edouard, who's the President CM of LookBookHQ, welcome to theCUBE. >> Thank you very much-- >> I was talking hockey, so I'm all distracted, I'm ruined, I'm disappointed, good to see you, before we get into it, it's all intelligent content, some of the things that are going on on this platform. But take a minute to talk about what your company does so we have some context. >> Yeah, absolutely. Well, we're a marketing technology company based in Toronto. We are a very big part of the Oracle Marketing App Cloud. And we kind of pick out where Oracle basically leaves off, where the Marketing Cloud stops. So a lot of what happens in the Marketing Cloud is focused on generating moments of attention and orchestrating that kind of buyer's journey. We're what happens in the destination side of the click. So we focus on the intelligent use of content. How do we deliver content? We think of every moment of attention as core to a marketer, that that really is their currency, the attention. We need to actually think that marketers, B2B marketers in particular, need to think a lot more like B2C, to think more like publishers. They're obsessed with attention. We shouldn't be satisfied with clicks or form fills. We need to actually be capitalizing on those moments of attention to make sure that if Bob is really my whitepaper, then I need to know that he's actually reading it. How do I then move him to the next best content asset, or give him a choice of content assets in session? So in essence what we do, what our company does, and we help companies like Thomson Reuters and ADP, in fact ADP is speaking on about half this afternoon, Polycom, Quintiles, whole host of big Eloqua customers. What we do is to help them take their content use model from something that looks and feels like Blockbuster, one size fits all, I don't know if actually Bob watched the video that he walked out of the shop with or the DVD, rather. And hence, why Blockbuster is RIP. And we take that and make it far more like Netflix. We make it far more on demand. >> Instrumented. >> Yeah, it's very much like-- >> This is interesting, the attention to impact is interesting, and you know, attention is essentially aided awareness, which is the Holy Grail in marketing, right? I mean, getting people to have some aid to a final destination or transaction of some sort. Am I getting that right? >> Yeah, very much so. I mean in the B2B side, obviously a marketer's job is to generate high quality marketing qualified leads and the real emphasis is on the Q, qualified. But companies like SiriusDecisions report that 94% of marketing qualified leads don't close. And that's a damning statistic for a B2B marketer. And our whole hypothesis is, and we're proving this to our customers is the reason why that happens is they're not qualified. Bob might have clicked on an e-mail, he might have filled in a form, et cetera, but did he read the content? We need to get an MQL to engage with five, seven, ten pieces of content to become a high quality MQL. And if we're only doing that one piece of content every engagement, that's really hard to do. No wonder our sales stat was this low. >> Well, we also need to understand that there's a progression people go through as they learn. It's not just that we want them to click on nine pieces of content-- >> Absolutely. >> As much as we want to see a pattern. So they've read this content and there was a suggestion made or an option provided and they then took the option, which is an even stronger suggestion that they've absorbed it, they've internalized it, and they're now part of a journey. So how does, I really like the idea that we're on the delivery side of the click, so you're, you know, we got all the stuff that's happening on the presenting things, the options, and then you're ensuring that when they click, whatever is being delivered is the highest quality-- >> Yeah. >> In terms of driving that customer forward in the journey, have I got that right? >> Yeah, absolutely. So if we take, if you take something which Eloqua's done a fantastic job, for example, of teaching their community of their customers about lead nurture and the typical nurture track looks and feels something like six emails over six weeks advertising content offer A, then content offer B, content offer C. And typically they're scheduled 10:00 a.m on a Thursday. Now this would be great, if we could get Bob and the other 12,000 people that we sent the email to to click on every single one of those emails. And the reality is, if I've got low, single-digit clickthrough rate, it's not going to happen. So what we do instead is, well, if they're engaging with A, why don't we give them B in the same session? While they're here, while we've done the hard work in getting their attention. And to your point, Peter, we're tracking that, and then we can start to make some really intelligent decisions going forward, as to, oh it worked, this is what resonated with Bob. This content asset's actually performed well. So we have two basic approaches to this. One is we let the marketer curate that experience, decide what A, B, C is et cetera. Or we actually use machine learning to auto-generate. If Bob arrives at A, what should B, C, and D et cetera be? So to that, it's very much like Netflix, where we kind of base our algorithm, it looks and feels very similar to Netflix's content discovery. That's great for anonymous or net new prospects in the top of your funnel, once you've figured out what they're actually interested in, then you can actually-- >> How does it work for you guys? Do you require registration, because content in these days has two flavors, free and gated. So there's always that dilemma, how much is free, you want some flow, tension, and then you go on conversion, gated, maybe premium content, how do you guys view that, is that part of or independent of what you do? >> No, it's a big part of what we do. And, one of the things, one of the capabilities that we have in our application is actually the ability to serve forms based on time and behavior. So, if they've engaged with three content assets, then maybe I actually want them to give me some more information or put up a hand. So we can make the form time-based, you can let them try before you buy, as it were, or you can hard gate it. We use Eloqua Forms in our application, so all that information flows as normal, as the work flows all get triggered, et cetera. It's very much up to the marketer how they want to use it, but one of the things, what we increasingly see amongst our customers, is the most successful do try to take the forms out of the way. Once we've got, once they are a member of our known database, how much more information do we really need them to volunteer, particularly with our ability to augment that contact record with other sources of data. Asking the marketer, I'm sorry, asking the prospect for it isn't always the most sensible thing to do. >> It's the free versus registration, but it's also new kinds of content. One of the things I like to say is software is content. Trying software, for example, is content. Or presenting an interactive experience that has a software element associated with it is a crucial part of gaging where people are. Are you able to start embedding your tooling directly into some of these more interactive elements and choose options within that interaction, or is it more options on static content? >> It could be both. So we are fundamentally content diagnostic. The best way to think about what we do is really as a very smart wrapper that goes around the content and then that can be embedded or it could be shared however you want, it could be used as a destination in its own right. So, sure if you want to kick something off with some former interactive content, absolutely. We also pull all different types of content together. So if your content is distributed or you want to use third party content, reviews, an expert in the space that's writing about something, and you pull that in and use that as a jumping off point. And what's really interesting is frequently it is the sequence of content that's the most interesting thing, not the behavior or engagement of a single asset. >> Right, and so part of the experience that sort of marketing is developing here translates into other disciplines within the business, for example, service. Come into respects, one of the things that you're presumably testing is is the prospect learning the right stuff that actually makes them more qualified. Well the same thing could be said for service, self-service. Is the person going through the right sequencing? Are you also seeing a demand for this kind of a product over on the service side and does that tie marketing back together? >> That's a great question. And one of the things that when we kind of officially launched our company in the application, three or four years ago now, we focused very much upon demand generation. Like we knew the problem that were helping themselves, but there are a handful of our customers, Cisco being one, actually, where actually at the moment, all they do is use us on the customer marketing side of things. How do I drive adoption, how do I drive cross-sell and upsell? I mean, all this is, we've got to remember that attention's what we're looking for and the way that we achieve that is using content as our primary asset as a marketer. The channels are important, the creative is important, but these really are content offers. Bob doesn't buy because he clicks on an email, Bob buys because he reads the stuff and watches the things. >> Peter: But it's attention and competence. >> Yeah. >> Right, so it's, Sy Syms used to say, I think it was Sy Syms, used to say that an informed customer is the best customer. You want a competent customer. >> Nick: Yeah. >> In many respects, the process of moving from the marketing qualified an MQL to an SQL it is, is that customer competent enough to actually engage with a sales person or somebody else to do something. >> That is spot on. So what we're seeing across our customer base is improvement in conversion rates from MQL to SAL for example. So McAfee, Intel Security, now McAfee again, they've seen a three times increase in the MQL to SAL conversion rates. Rockwell Automation has actually made a 300x return on their investment in us in nine months by passing higher qualified leads to the sales team. I think they generated $250 million in additional net-- >> Peter: Rockwell? >> Rockwell, yeah. ADP, that's doing our customer case study this afternoon, a 3x increase in marketing influence opportunities, and a 6x increase in closed won marketing opportunities. So more, but to your point here, better qualified. We know that these people are actually read our stuff, therefore the conversation is easier. They are actually generally qualified. Carrier's been proving that out by actually 2.4 times faster through the funnel to MQL, and then their ACP is 2.3 times higher. Why? Because they're not getting the pushback in the sale cycle because the prospect has self-educated and they see the value now. >> Nick, I want to get your thoughts on something that's involved in our, we're in a independent media company and we don't really have any ads on our site at all, but we have a sponsorship model, we have data. But it's interesting, I'm reading an Ad Age article right now that says for the first time ever, digital has surpassed TV. I mean, I can remember-- >> Wow. back in the days, it's always this little slice and it's getting bigger and bigger. But for the first time, desktop and mobile ad revenue surpasses TV for the first time, 22% upswing from the previous year. So, digital ads, some are calling it native advertisement, whatever the hell that means, is a key part of the attention cycle. So the role that a marketer needs to take with channels as important, and a lot of content marketers are failing these days that we talk to because they're not being authentic with their message and the users can smell-- >> Right. >> You know, non-relevant content. Some are clever and make it link-baitish and some are actually really super smart and actually do authentic content. But, so that's kind of progression. That's an evolution in the industry, but from a data standpoint, there are platforms out there, like SiliconANGLE and others, that have an opportunity for impression and attention in real time. How does your system, how does your clients, and how do people deal with that? Is there a way, is there mechanisms? >> So we have two large publisher customers that run multiple different properties and have very large communities they're looking to monetize and they're all part of the Oracle Marketing Cloud, they use their tech stat, and we're helping them in two ways. Firstly, kind of from an advertising thing, like high value added, advertising solutions, for want of a better description. How do I help to monetize my community, not just pass to HP, if they're the advertiser, here's a list of names of people that filled in the form. Here's actually people that are engaging with your content. And it's a mix of our editorial and your content to tell a story. And then one of the things that we're starting to explore for them is actually far more on the native side of things, actually being embedded as... >> John: An asset? >> Essentially, as a native ad in its own right, which can kind of get launched. That's something which I'm keen to explore further. And at the heart of it is, it's probably an even bigger problem on the ad tech side that it is on the martech side, but people like Gary Vaynerchuk are starting to ask the ad tech industry, we need a dose of common sense here, was the marketing consumed? And that's something which I am fascinated with, we're starting to see that we can actually identify by channels. This channel might, well this particular display provide the SP may have generated, you say, oh I don't know, 1,000 clicks in the last 30 days. Did it do anything? Did my-- >> A lot of times valuable. >> Exactly. >> You know at the end of the day, to sustain attention, you have to be valuable. I think John, we're talking really about a continuum from impression to attention to competence. We want to work with competent buyers because it cuts down the time that we spend on it, it reduces the risk that we're wasting our time, and quite frankly, it's a lot easier to work with someone who's really engaged and wants to succeed with whatever we're offering. >> It's also, he mentioned the publisher angle, I was thinking also from the customer angle, because I'm a customer and a marketer, I'm going to be looking for mechanisms to go to. The publisher wants better monetization of their communities, so have you seen any patterns in the business that could be a use case for helping customers operationalize, and we had great success with our business in the sense of saying, hey, we're engaging users, so that's good, you should join in with us at the right time not, you know, try to do it six hours too late, right, it's like being late to the party, right. So that real time piece is really super important. >> For sure. We've actually just changed the way that we're integrating with Eloqua to speed that up. So now we've actually moved to using web hooks as part of the integration and using their map form processing capabilities. Because it's faster, it's more extensive, it's more scalable. It means we can get very rich in content engagement data into someone's hands faster and better. And, I think, what is it? 50% of people buy from the first person that shows up, so being able to do that is critically important. >> Member-based communities are getting a lot of trends, traction these days. Some call, you know, subscribers, buyer walls, but member-based. >> So something we're starting to look at is how do we actually start to auto-generate the content experience, yeah, around kind of key accounts or topics, et cetera. >> Fascinating conversation, Nick, appreciate it, coming on. >> Nick: My pleasure. >> LookBookHQ, check 'em out, doing intelligent content, scaling content, looking at data, congratulations on your success, look forward to following up with you on some of the native advertising solutions that we, me need, that you need and congratulations, Oracle's certainly taking advantage of it. >> See you next time. Cheers. >> Thanks for coming out. Be back with more live coverage, I'm John Furrier, Peter Burris after this short break. (techno music)

>> It's the Cube covering IBM cheap Data Officer Strategy Summit brought to you by IBM. Now, here are your hosts Day villain day and still minimum. >> Welcome back to Boston, everybody. This is the Cube, the worldwide leader in live tech coverage. We here at the Chief Data Officers Summit that IBM is hosting in Boston. I'm joined by Courtney Abercrombie. According your your title's too long. I'm just gonna call you a cognitive rockstar on >> Alec Crane is >> here from Yusa. System by President, Vice President at that firm. Welcome to the Cube. Great to see you guys. Thank you. So this event I love it. I mean, we first met at the, uh, the mighty chief data officer conference. You were all over that networking with the CEO's helping him out and just really, I think identified early on the importance of this constituency. Why? How did you sort of realize and where have you taken it? >> It's more important than it's ever been. And we're so grateful every time that we see a new chief data officer coming in because you just can't govern and do data by committee. Um, if you really hope to be transformational in your company. All these huge, different technologies that are out there, All this amazing, rich data like weather data and the ability to leverage, you know, social media information, bringing that all together and really establishing an innovation platform for your company. You can't do that by committee. You really have to have a leader in charge of it. and that’s what chief data officers are here to do. And so every time we see one, we're so grateful >> that just so >> that we just heard from Inderpal Bhandari on his recommendation for how you get started. It was pretty precise and prescriptive. But I wonder, Alan. So tell us about the chief data officer role at USAA. Hasn't been around for a while. Of course, it's a regulated business. So probably Maur, data oriented are cognizant than most businesses. But tell us about your journey. >> We started probably about 4 or 5 years ago, and it was a combination of trying to consolidate data and analytics operations and then decentralized them, and we found that there was advantages and pros and cons of doing both. You'd get the efficiencies, but once you got the efficiencies, you'd lose the business expertise, and then we'd have to tow decentralize. So we ended up landing a couple of years ago. What we call a hub and spoke system where we have centralized governance and management of key data assets, uh, data modelling data science type work. And then we still allow the, uh, various lines of business to have their own data offices. And the one I run for USAA is our distribution channels office for all of the data and analytics. And we take about 100,000,000 phone calls a year. About 2,000,000,000 webb interactions. Mobile interactions. We take about 18,000 hours. That's really roughly two years of phone conversation data in per day. Uh, we take about 50,000,000 lines of, uh, Web analytic traffic per day as well. So trying to make sense of that to nurture remember, relationships, reinforce trust and remove obstacles >> for your supporting the agent systems. Is that right? >> I support the agent systems as well as the, um, digital >> systems. Okay. And so the objective is obviously toe to grow the business, keep it running, keep the customers happy. Very operate, agent Just efficient. Okay. Um and so when you that's really interesting. This sort of hub and spoke of decentralization gets you speed and closer to the business. Centralization get you that that efficiency. Do you feel like you found that right balance? I mean, if you think so. I >> think you know, early on, we it was mme or we had more cerebral alignment, you know, meaning that it seemed logical to us. But actually, once the last couple of years, we've had some growing pains with roles, responsibilities, overlaps, some redundancy, those types of things. But I think we've landed in a good place. And that's that's what I'm pretty proud of because we've been able to balance the agility with the governance necessary toe, have good governance and put in place, but then also be able to move at the speed the businessmen. >> So Courtney, one of things we heard one of the themes this morning within IBM it's of the role of the chief Data officer's office is to really empower the lines of business with data so that you can empower your customers is what Bob Tatiana was telling us, right? With data. So how are you doing? That is you have new services. You have processes or how is that all working >> right? We dio We have a lot of things, actually, because we've been working so much with people like Allen's group who have been leaders at, quite frankly, in establishing best practices on even how to set up these husbands votes. A lot of people are, you know, want to talk, Teo, um, the CDO and they've spun off even a lot of CEOs into other organizations, in fact, but I mean, they're really a leader in this area. So one of the things that we've noticed is you know, the thing that gives everybody the biggest grief is trying to figure out how to work with unstructured data. Um, and all this volume of data, it's just insane. And just like I was saying in the panel earlier, only about 5% of your actual internal data is enough to actually create a context around your customers. You really have to be able to go with all this exogenous data to understand what were the bigger ramifications that were going on in any customer event, whether it's a call in or whether it's, uh, you know, I'm not happy today with something that you tried to sell me or something that you didn't respond too fast enough, which I'm sure Alan could, you know, equate to. But so we have this new data as a service that we've put together based on the way the weather data has, the weather company has put their platform together. We're using a lot of the same kind of like micro services that you saw Bob put on the screen. You know, everything from, I mean, open source. As much open sources we can get, get it. And it's all cloud based. So and it's it's ways to digest and mix up both that internal data with all of that big, voluminous external data. >> So I'm interested in. So you get the organizational part down. Least you've settled on approach. What are some of the other big challenges that you face in terms of analytics and cognitive projects? Your organization? How are you dealing with those? >> Well, uh, >> to take a step back, use a We're, uh, financial services company that supports the military and their families. We now have 12 million members, and we're known for our service. And most of the time, those moments of truth, if you will, where our service really shines has been when someone talks to you, us on the phone when those member service reps are giving that incredible service that they're known for on the reason being is that the MSR is the aggregator of all that data. When you call in, it's all about you. There's two screens full of your information and the MSR is not interested in anything else but just serving you, our digital experiences more transactional in orientation. And it was It's more utilitarian, and we're trying to make it more personal, trying to make it more How do we know about you? And so one of the cues that were that were taking from the MSR community through cognitive learning is we like to say the only way to get into the call is to get into the call, and that is to truly get into the speech to text, Then do the text mining on that to see what are the other topics that are coming out that could surface that we're not actually capturing. And then how do we use those topics at a member level two then help inform the digital experience to make it more personal. How do I detect life events? Our MSR's are actually trained to listen for things like words like fiance, marriage moving, maybe even a baby crying in the background. How do we take that knowledge and turn that into something that machine learning can give us insights that can feedback into our digital transact actions. So >> this's what our group. >> It's a big task. So So how are >> you doing that? I mean, it's obviously we always talk about people processing technology. Yeah, break that down for us. I mean, how are you approaching that massive opportunity? >> Part of it is is, uh, you know, I look at it. It is like a set of those, you know, Russian nesting dolls. You know, every time you solve one problem, there's another problem inside of it. The first problem is getting access to the data. You know, where and where do you store? We're taking in two years of data per day of phone call data into a system where you put all that right and then you're where you put a week's worth a month's worth a quarter's worth of data like that. Then once you solve that problem, how do you read Act all that personal information So that that private information that you really don't need that data exhaust that would actually create a liability for you in our in our world so that you can really stay focused on what of the key themes that the member needs? And then the third thing is now had. Now that you've got access to the data, it's transcribed for you. It's been redacted from its P I I type work well, now you need the horse power and of analysts on, we're exploring partnerships with IBM, both locally and in in the States as well as internationally to look at data science as a service and try to understand How can we tap into this huge volume of data that we've got to explore those types of themes that are coming up The biggest challenges in typical transaction logging systems. You have to know what your logging You have to know what you're looking for before you know what to put the date, where to put the data. And so it's almost like you kind of have to already know that it's there to know how much you're acquiring for it and what we need to do more as we pivot more towards machine learning is that we need the data to tell us what's important to look at. And that's really the vat on the value of working with these folks. >> So obviously, date is increasingly on structure we heard this morning and whatever, 80 90% is structured. So here you're no whatever. You're putting it into whatever data fake swamp, ocean, everything center everywhere, and you're using sort of machine learning toe both find signal, but also protected yourself from risk. Right. So you've got a T said you gotta redact private information. So much of that information could be and not not no schema? Absolutely. Okay, So you're where are you in terms of solving that problem in the first inning or you deeper than that, >> we're probably would say beyond the first inning, but we so we've kind of figured out what that process is to get the data and all the piece parts working together. We've made some incredible insights already. Things that people, you know, I had no idea that was there. Um, but, uh, I'd say we still have a long way to go. Is particularly terms of scaling scaling the process, scaling the thie analytics, scaling the partnerships, figuring out how do we get the most throughput? I would say it's It's one of those things. We're measuring it on, maybe having a couple of good wins this year. A couple of really good projects that have come across. We want to kind of take that tube out 10 projects next year in this space. And that's how we're kind of measuring the velocity and the success >> data divas. I walked away and >> there was one of them Was breakfast this morning. Data divas. You hold this every year. >> D'oh! It's growing. Now we got data, >> dudes. So I was one of the few data dudes way walked in >> one of the women chief date officers. I got no problem with people calling me a P. >> I No. Yeah, I just sell. Sit down. Really? Bath s o. But also, >> what's the intent of that? What learning is that you take out of those? >> I think it's >> more. It's You know, you could honestly say this isn't just a data Debo problem. This is also, you know, anybody who feels like they're not being heard. Um, it's really easy to get drowned out in a lot of voices when it comes to data and analytics. Um, everybody has an opinion. I think. Remember, Ursula is always saying, Ah, all's fair in love, war and data. Um and it feels like, you know, sometimes you go, I'll come to the table and whoever has the loudest voice and whoever bangs their test the loudest, um, kind of wins the game. But I think in this case, you know, a lot of women are taking these roles. In fact, we saw, you know, a while back from Gardner that number about 25% of chief data officers are actually women because the role is evolving out of the business lines as opposed Thio more lines. And so I mean, it makes sense that, you know, were natural collaborators. I mean, like the biggest struggle and data governance isn't setting up frameworks. It's getting people to actually cooperate and bring data to the table and talk about their business processes that support that. And that's something that women do really well. But we've got to find our voice and our strength and our resolve. And we've got to support each other in trying to bring more diverse thinking to the table, you know? So it's it's all those kinds of issues and how do you balance family? I mean, >> we're seeing >> more and more. You know, I don't know if you know this, but there's actual statistics around millennials and that males are actually starting to take on more more role of being the the caregiver in the family. So I mean as we see that it's an interesting turnabout because now all the sudden, it's no longer, you know, women having that traditional role of, you know, I gotta always be home. Now we're actually starting to see a flip of that, which is which is, >> You know, I think it's kind of welcome. My husband's definitely >> I say he's a better parent than me. >> Friday. It's >> honest he'll watch this and he >> can thank me later that it was >> a great discussion this morning. Alan, I want to get your feedback on this event and also you participate in a couple of sessions yesterday. Maybe you could share with our audience Some of the key takeaways in the event of general and specific ones that you worked on yesterday. >> Well, I've been fortunate to come to the event for a couple of years now. And when we were just what 50 or so of us that were showing up? So, you know, I see that the evolution just in a couple of years time conversations have really changed. First meeting that we had people were saying, Where do you report in the organization? Um, how many people do you have? What do you do for your job? They were very different answers to any of that everywhere. From I'm an independent contributor that's a data evangelist to I run legions of data analysts and reporting shops, you know, and so forth and everything in between. And so what I see what it's offers in first year was really kind of a coalescing of what it really means to be a data officer in the company that actually happened pretty quickly in my mind, Um, when by seeing it through through the lens of my peers here, the other thing was when you when you think about the topics the topics are getting a lot more pointed. They're getting more pointed around the monetization of data communicating data through visualization, storytelling, key insights that you, you know, using different technologies. And we talked a lot yesterday about storytelling and storytelling is not through visual days in storytelling is not just about like who has the most, you know, colors on on a slide or or ah you know, animation of your bubble charts and things like that. But sometimes the best stories are told with the most simple charts because they resonate with your customers. And so what I think is it's almost like kind of getting a back to the basics when it comes to taking data and making it meaningful. We're only going to grow our organizations and data and data scientists and analysts. If we can communicate to the rest of the organization, our value and the key to creating that value is they can see themselves in our data. >> Yeah, the visit is we like to call it sometimes is critical to that to that storytelling. Sometimes I worry and we go onto these conferences and you go into a booth and look what we can do with machine learning, and we would just be looking at just this data. So what do I do? What >> I do with all this? Yeah. >> I don't know how it would make sense of it. So So is there a special storyteller role within your organization or you all storytellers? Do you cross train on that? Or >> it's funny you'd ask that one of the gentlemen of my team. He actually came to me about six months ago, and he says I'm really good at at the analysis part, but I really have a passion for things like Photoshopped things like, uh uh, uh the various, uh, video and video editing type software. He says I want to be your storyteller. I want to be creating a team of data and analytics storytellers for the rest of the organization. So we pitched the idea to our central hub and spoke leadership group. They loved it. They loved the idea. And he is now, um, oversubscribed. You would say in terms of demand for how do you tell the data? How do you tell the data story and how it's moving the business forward? And that takes the form kind of everything from infographics tell you also about how do you make it personal when, when? Now 7,000 m s. Ours have access to their own data. You know, really telling that at a at a very personal level, almost like a vignette of animus are who's now able to manage themselves using the data that they were not able able tto have before we're in the past, only managers had access to their performance results. This video, actually, you know, pulls on the heartstrings. But it it not only does that, but it really tells the story of how doing these types of things and creating these different data assets for the rest of your organization can actually have a very meaningful benefit to how they view work and how they view autonomy and how they view their own personal growth. >> That's critical, especially in a decentralized organization. Leased a quasi decentralized organization, getting everybody on the same page and understand You know what the vision is and what the direction is. It s so often if you don't have that storytelling capability, you have thousands of stories, and a lot of times there's dissonance. I mean, I'm not saying there's not in your in your organization, but have you seen the organization because of that storytelling capability become Mohr? Yeah, Joe. At least Mohr sort of effective and efficient, moving forward to the objectives. Well, >> you know, as a as a data person, I'm always biased thatyou know data, you know, can win an argument if presented the right way. It's the The challenge is when you're trying to overcome or go into a direction. And in this case, it was. We wanted to give more autonomy. Toothy MSR community. Well, the management of that call center were 94 year old company. And so the management of that of that call center has been doing things a certain way for many, many, many, many years. And the manager's having access to the data. The reps not That was how we did things, you know. And so when you make a change like that, there's a lot of hesitation of what is this going to do to us? How is this going to change? And what we're able to show with data and with through these visualizations is you really don't have anything to worry about? You're only gonna have upside, you know, in this conversation because at the end of the day, what's going to empower people this having access and power of >> their own destiny? Yeah, access is really the key isn't because we've all been in the meetings where somebody stands up and they've got some data point in there pounding the table, >> right? Oftentimes it's a man, all right. It >> is a powerful pl leader on jamming data down your throats, and you don't necessarily know the poor sap that he's, you know, beating up. Doesn't think Target doesn't have access to the data. This concept of citizen data scientists begins to a level that playing field doesn't want you seeing that >> it does. And I want to actually >> come back to what you're saying because there's a larger thought there, which is that we don't often address, and that's this change banishment concept. I mean, we we look at all these. I mean, everybody looks at all these technologies and all this information, and how much data can you possibly get your >> hands on? But at the end of >> the day, it's all about trying to create an outcome. A some joint outcome for the business and it could be threatening. It could be threatening to the C suite people who are actually deploying the use of these data driven tools because >> it may go >> against their gut. And, you >> know, oftentimes the poor messenger of that, >> When when you have to be the one that stands up and go against that, that senior vice presidents got it, the one who's pounding and saying No, but I know better >> That could be a >> tough position to be in without having some sort of change management philosophy going on with the introduction of data and analytics and with the introduction of tools, because there's a whole reframing that, Hey, my gut instinct that got me here all the way to the top doesn't necessarily mean that it's going to continue to scale in this new world with all of all of our competitors and all these, you know, massive changes going on in the market place right now. My guts not going to get me there anymore. So it's hard, it's hard, and I think a lot of executives don't really know to invest in that change management, if you know that goes with it that you need to change philosophies and mindsets and slowly introduced visualizations and things that get people slowly onboard, as opposed to just throwing it at him and saying here, believe it. >> Think I mean, it wasn't that >> long ago. Certainly this this millennium, where you know, publications like Harvard Business Review had, uh, cover stories on why gut feel, you know, beats, you know, analysis by paralysis. >> That seems to be changing. And >> the data purists would say the data doesn't lie. It was long as you could interpret it correctly. Let the data tell us what to do, as opposed to trying to push an agenda. But they're still politics. >> There's just things out >> there that you can't even perceive of that air coming your way. I mean, like, Blockbuster Netflix, Alibaba versus standard retailers. I mean, >> there's just things out >> there that without the use of things like machine learning and being comfortable with the use, the things like mission learning a lot of people think of that kind of stuff is >> Well, don't get your >> hoodoo voodoo into my business. You know, I don't know what that algorithm stuff does. It's >> going Yeah, I mean, e. I mean to say, What the hell is this? And now, yeah, it's coming and >> you need to get ready. >> There's an >> important role, though I think instinct, you know, you don't want to dismiss a 20 year leader in a particular operations because they've they've they've getting themselves where they're at because in large part, maybe they didn't have all the data. But they learned through a lot of those things, and I think it's when you marry those things up. And if you kenbrell in a kind of humble way to that kind of leader and win them over and show how it may be validating some of their, um uh yeah, that some of their points Or maybe how it explains it in a different way. Maybe it's not exactly what they want to see, but it's helping to inform their business, and you come into him as a partner, as opposed to gotcha, you know. Then then you know you can really change the business that way. And >> what is it? Was Linda Limbic brain is it just doesn't feel right. Is that the part of the brain that informs you that? And so It's hard to sometimes put, but you're right. Uh, there there is a component of this which is gut feel instinct and probably relates to to experience. So it's It's like, uh, when, when, uh, Deep blue beat Garry Kasparov. We talk about this all the time. It turns out that the best chess player in the world isn't a machine. It's a It's a human in the machine. >> That's right. That's exactly right. It's always the training that people training these things, that's where it gets its information. So at the end of the day, you're right. It's always still instinct to some >> level. I could We gotta go. All right. Last word on the event. You know what's next? >> Don't love my team. Data officer. Miss, you guys. It is good >> to be here. We appreciate it. All right, We'll leave it there. Thank you, guys. Thank you. All right, keep right. Everybody, this is Cuba. Live from IBM Chief Data Officer, Summit in Boston Right back. My name is Dave Volante.