from around the globe it's thecube presenting the convergence of file and object brought to you by pure storage we're back with the convergence of file and object a special program made possible by pure storage and co-created with the cube so in this series we're exploring that convergence between file and object storage we're digging into the trends the architectures and some of the use cases for unified fast file and object storage uffo with me is matt burr who's the vice president general manager of flashblade at pure storage hello matt how you doing i'm doing great morning dave how are you good thank you hey let's start with a little 101 you know kind of the basics what is unified fast file and object yeah so look i mean i think you got to start with first principles talking about the rise of unstructured data so when we think about unstructured data you sort of think about the projections 80 of data by 2025 is going to be unstructured data whether that's machine generated data or you know ai and ml type workloads you start to sort of see this i don't want to say it's a boom uh but it's sort of a renaissance for unstructured data if you will where we move away from you know what we've traditionally thought of as general purpose nas and and file shares to you know really things that focus on uh fast object taking advantage of s3 cloud native applications that need to integrate with applications on site um you know ai workloads ml workloads tend to look to share data across uh you know multiple data sets and you really need to have a platform that can deliver both highly performant and scalable fast file and object from one system so talk a little bit more about some of the drivers that you know bring forth that need to unify file an object yeah i mean look you know there's a there's there's a real challenge um in managing you know bespoke uh bespoke infrastructure or architectures around general purpose nas and daz etc so um if you think about how a an architect sort of looks at an application they might say well okay i need to have um you know fast daz storage proximal to the application um but that's gonna require a tremendous amount of dabs which is a tremendous amount of drives right hard drives are you know historically pretty pretty pretty unwieldy to manage because you're replacing them relatively consistently at multi-petabyte scale so you start to look at things like the complexity of das you start to look at the complexity of general purpose nas and you start to just look at quite frankly something that a lot of people don't really want to talk about anymore but actual data center space right like consolidation matters the ability to take you know something that's the size of a microwave like a modern flash blade or a modern um you know uffo device replaces something that might be you know the size of three or four or five refrigerators so matt why is is now the right time for this i mean for years nobody really paid much attention to object s3 already obviously changed you know that course most of the world's data is still stored in file formats and you get there with nfs or smb why is now the time to think about unifying object and and file well because we're moving to things like a contactless society um you know the the things that we're going to do are going to just require a tremendous amount more compute power network and quite frankly storage throughput and you know i can give you two sort of real primary examples here right um you know warehouses are being you know taken over by robots if you will um it's not a war it's a it's a it's sort of a friendly advancement in you know how do i how do i store a box in a warehouse and you know we have we have a customer who focuses on large sort of big box distribution warehousing and you know a box that carried a an object uh two weeks ago might have a different box size two weeks later well that robot needs to know where the space is in the data center in order to put it but also needs to be able to process hey i don't want to put the thing that i'm going to access the most in the back of the warehouse i'm going to put that thing in the front of the warehouse all of those types of data you know sort of real time you can think of the robot as almost an edge device uh is processing in real time unstructured data and its object right so it's sort of the emergence of these new types of workloads and i give you the opposite example the other end of the spectrum is ransomware right you know today you know we'll talk to customers and they'll say quite commonly hey if you know anybody can sell me a backup device i need something that can restore quickly if you had the ability to restore something in 270 terabytes an hour or 250 terabytes an hour that's much faster when you're dealing with a ransomware attack you want to get your data back quickly you know so i want to actually i was going to ask you about that later but since you brought it up what is the right i guess call it architecture for for for ransomware i mean how and explain like how unified object and file would support me i get the fast recovery but how would you recommend a customer uh go about architecting a ransomware proof you know system yeah well you know with with flashblade and and with flasharray there's an actual feature called called safe mode and that safe mode actually protects uh the snapshots and and the data from uh sort of being is a part of the of the ransomware event and so if you're in a type of ransomware situation like this you're able to leverage safe mode and you say okay what happens in a ransomware attack is you can't get access to your data and so you know the bad guy the perpetrator is basically saying hey i'm not going to give you access to your data until you pay me you know x in bitcoin or whatever it might be right um with with safe mode those snapshots are actually protected outside of the ransomware blast zone and you can bring back those snapshots because what's your alternative if you're not doing something like that your alternative is either to pay and unlock your data or you have to start retouring restoring excuse me from tape or slow disk that could take you days or weeks to get your data back so leveraging safe mode um you know in either the flash for the flash blade product is a great way to go about uh architecting against ransomware i got to put my i'm thinking like a customer now so safe mode so that's an immutable mode right can't change the data um is it can can an administrator go in and change that mode can he turn it off do i still need an air gap for example what would you recommend there yeah so there there are still um uh you know sort of our back or rollback role-based access control policies uh around who can access that safe mode and who can right okay so uh anyway subject for a different day i want to i want to actually bring up uh if you don't object a topic that i think used to be really front and center and it now be is becoming front and center again i mean wikibon just produced a research note forecasting the future of flash and hard drives and those of you who follow us know we've done this for quite some time and you can if you could bring up the chart here you you could see and we see this happening again it was originally we forecast the the death of of quote unquote high spin speed disk drives which is kind of an oxymoron but you can see on here on this chart this hard disk had a magnificent journey but they peaked in volume in manufacturing volume in 2010 and the reason why that is is so important is that volumes now are steadily dropping you can see that and we use wright's law to explain why this is a problem and wright's law essentially says that as you your cumulative manufacturing volume doubles your cost to manufacture decline by a constant percentage now i won't go too much detail on that but suffice it to say that flash volumes are growing very rapidly hdd volumes aren't and so flash because of consumer volumes can take advantage of wright's law and that constant reduction and that's what's really important for the next generation which is always more expensive to build and so this kind of marks the beginning of the end matt what do you think what what's the future hold for spinning disc in your view uh well i can give you the answer on two levels on a personal level uh it's why i come to work every day uh you know the the eradication or or extinction of an inefficient thing um you know i like to say that inefficiency is the bane of my existence uh and i think hard drives are largely inefficient and i'm willing to accept the sort of long-standing argument that um you know we've seen this transition in block right and we're starting to see it repeat itself in in unstructured data um and i'm willing to accept the argument that cost is a vector here and it most certainly is right hdds have been considerably cheaper uh than than than flash storage um you know even to this day uh you know up to this point right but we're starting to approach the point where you sort of reach a 3x sort of you know differentiator between the cost of an hdd and an sdd and you know that really is that point in time when uh you begin to pick up a lot of volume and velocity and so you know that tends to map directly to you know what you're seeing here which is you know a slow decline uh which i think is going to become even more rapid kind of probably starting around next year where you start to see sds excuse me ssds uh you know really replacing hdds uh at a much more rapid clip particularly on the unstructured data side and it's largely around cost the the workloads that we talked about robots and warehouses or you know other types of advanced machine learning and artificial intelligence type applications and workflows you know they require a degree of performance that a hard drive just can't deliver we are we are seeing sort of the um creative innovative uh disruption of an entire industry right before our eyes it's a fun thing to live through yeah and and we would agree i mean it doesn't the premise there is it doesn't have to be less expensive we think it will be by you know the second half or early second half of this decade but even if it's a we think around a 3x delta the value of of ssd relative to spinning disk is going to overwhelm just like with your laptop you know it got to the point where you said why would i ever have a spinning disc in my laptop we see the same thing happening here um and and so and we're talking about you know raw capacity you know put in compression and dedupe and everything else that you really can't do with spinning discs because of the performance issues you can do with flash okay let's come back to uffo can we dig into the challenges specifically that that this solves for customers give me give us some examples yeah so you know i mean if we if we think about the examples um you know the the robotic one um i think is is is the one that i think is the marker for you know kind of of of the the modern side of of of what we see here um but what we're you know what we're what we're seeing from a trend perspective which you know not everybody's deploying robots right um you know there's there's many companies that are you know that aren't going to be in either the robotic business uh or or even thinking about you know sort of future type oriented type things but what they are doing is greenfield applications are being built on object um generally not on not on file and and not on block and so you know the rise of of object as sort of the the sort of let's call it the the next great protocol for um you know for uh for for modern workloads right this is this is that that modern application coming to the forefront and that could be anything from you know financial institutions you know right down through um you know we've even see it and seen it in oil and gas uh we're also seeing it across across healthcare uh so you know as as as companies take the opportunity as industries to take this opportunity to modernize you know they're modernizing not on things that are are leveraging you know um you know sort of archaic disk technology they're they're they're really focusing on on object but they still have file workflows that they need to that they need to be able to support and so having the ability to be able to deliver those things from one device in a capacity orientation or a performance orientation while at the same time dramatically simplifying the overall administration of your environment both physically and non-physically is a key driver so the great thing about object is it's simple it's a kind of a get put metaphor um it's it scales out you know because it's got metadata associated with the data uh and and it's cheap the drawback is you don't necessarily associate it with high performance and and as well most applications don't you know speak in that language they speak in the language of file you know or as you mentioned block so i i see real opportunities here if i have some some data that's not necessarily frequently accessed you know every day but yet i want to then whether end of quarter or whatever it is i want to i want to or machine learning i want to apply some ai to that data i want to bring it in and then apply a file format uh because for performance reasons is that right maybe you could unpack that a little bit yeah so um you know we see i mean i think you described it well right um but i don't think object necessarily has to be slow um and nor does it have to be um you know because when you think about you brought up a good point with metadata right being able to scale to a billions of objects being able to scale to billions of objects excuse me is of value right um and i think people do traditionally associate object with slow but it's not necessarily slow anymore right we we did a sort of unofficial survey of of of our of our customers and our employee base and when people described object they thought of it as like law firms and storing a word doc if you will um and that that's just you know i think that there's a lack of understanding or a misnomer around what modern what modern object has become and perform an object particularly at scale when we're talking about billions of objects you know that's the next frontier right um is it at pace performance wise with you know the other protocols no but it's making leaps and grounds so you talked a little bit more about some of the verticals that you see i mean i think when i think of financial services i think transaction processing but of course they have a lot of tons of unstructured data are there any patterns you're seeing by by vertical market um we're you know we're not that's the interesting thing um and you know um as a as a as a as a company with a with a block heritage or a block dna those patterns were pretty easy to spot right there were a certain number of databases that you really needed to support oracle sql some postgres work etc then kind of the modern databases around cassandra and things like that you knew that there were going to be vmware environments you know you could you could sort of see the trends and where things were going unstructured data is such a broader horizontal um thing right so you know inside of oil and gas for example you have you know um you have specific applications and bespoke infrastructures for those applications um you know inside of media entertainment you know the same thing the the trend that we're seeing the commonality that we're seeing is the modernization of you know object as a starting point for all the all of the net new workloads within within those industry verticals right that's the most common request we see is what's your object roadmap what's your you know what's your what's your object strategy you know where do you think where do you think object is going so um there isn't any um you know sort of uh there's no there's no path uh it's really just kind of a wide open field in front of us with common requests across all industries so the amazing thing about pure just as a kind of a little you know quasi you know armchair historian the industry is pure was really the only company in many many years to be able to achieve escape velocity break through a billion dollars i mean three part couldn't do it isilon couldn't do it compellent couldn't do it i could go on but pure was able to achieve that as an independent company uh and so you become a leader you look at the gartner magic quadrant you're a leader in there i mean if you've made it this far you've got to have some chops and so of course it's very competitive there are a number of other storage suppliers that have announced products that unify object and file so i'm interested in how pure differentiates why pure um it's a great question um and it's one that uh you know having been a long time puritan uh you know i take pride in answering um and it's actually a really simple answer um it's it's business model innovation and technology right the the technology that goes behind how we do what we do right and i don't mean the product right innovation is product but having a better support model for example um or having on the business model side you know evergreen storage right where we sort of look at your relationship to us as a subscription right um you know we're gonna sort of take the thing that that you've had and we're gonna modernize that thing in place over time such that you're not rebuying that same you know terabyte or you know petabyte of storage that you've that you that you've paid for over time so um you know sort of three legs of the stool uh that that have made you know pure clearly differentiated i think the market has has recognized that um you're right it's it's hard to break through to a billion dollars um but i look forward to the day that you know we we have two billion dollar products and i think with uh you know that rise in in unstructured data growing to 80 by 2025 and you know the massive transition that you know you guys have noted in in in your hdd slide i think it's a huge opportunity for us on you know the other unstructured data side of the house you know the other thing i'd add matt and i've talked to cause about this is is it's simplicity first i've asked them why don't you do this why don't you do it and the answer is always the same is that adds complexity and we we put simplicity for the customer ahead of everything else and i think that served you very very well what about the economics of of unified file and object i mean if you bringing additional value presumably there's a there there's a cost to that but there's got to be also a business case behind it what kind of impact have you seen with customers yeah i mean look i'll i'll go back to something i mentioned earlier which is just the reclamation of floor space and power and cooling right um you know there's a you know there's people people people want to search for kind of the the sexier element if you will when it comes to looking at how we how you derive value from something but the reality is if you're reducing your power consumption by you know by by a material percentage um power bills matter in big in big data centers you know customers typically are are facing you know a paradigm of well i i want to go to the cloud but you know the clouds are not being more expensive than i thought it was going to be or you know i've figured out what i can use in the cloud i thought it was going to be everything but it's not going to be everything so hybrid's where we're landing but i want to be out of the data center business and i don't want to have a team of 20 storage people to match you know to administer my storage um you know so there's sort of this this very tangible value around you know hey if i could manage um you know multiple petabytes with one full-time engineer uh because the system uh to your and kaza's point was radically simpler to administer didn't require someone to be running around swapping drives all the time would that be a value the answer is yes 100 of the time right and then you start to look at okay all right well on the uffo side from a product perspective hey if i have to manage a you know bespoke environment for this application if i have to manage a bespoke environment for this application and a spoke environment for this application and this focus environment for this application i'm managing four different things and can i actually share data across those four different things there's ways to share data but most customers it just gets too complex how do you even know what your what your gold.master copy is of data if you have it in four different places or you try to have it in four different places and it's four different siloed infrastructures so when you get to the sort of the side of you know how do we how do you measure value in uffo it's actually being able to have all of that data concentrated in one place so that you can share it from application to application got it i'm interested we use a couple minutes left i'm interested in the the update on flashblade you know generally but also i have a specific question i mean look getting file right is hard enough uh you just announced smb support for flashblade i'm interested in you know how that fits in i think it's kind of obvious with file and object converging but give us the update on on flashblade and maybe you could address that specific question yeah so um look i mean we're we're um you know tremendously excited about the growth of flashblade uh you know we we we found workloads we never expected to find um you know the rapid restore workload was one that was actually brought to us from from a customer actually um and has become you know one of our one of our top two three four you know workloads so um you know we're really happy with the trend we've seen in it um and you know mapping back to you know thinking about hdds and ssds you know we're well on a path to building a billion dollar business here so you know we're very excited about that but to your point you know you don't just snap your fingers and get there right um you know we've learned that doing file and object uh is is harder than block um because there's more things that you have to go do for one you're basically focused on three protocols s b nfs and s3 not necessarily in that order um but to your point about s b uh you know we we are on the path through to releasing um you know smb full full native smb support in in the system that will allow us to uh service customers we have a limitation with some customers today where they'll have an smb portion of their nfs workflow um and we do great on the nfs side um but you know we didn't we didn't have the ability to plug into the s p component of their workflow so that's going to open up a lot of opportunity for us um on on that front um and you know we continue to you know invest significantly across the board in in areas like security which is you know become more than just a hot button you know today security's always been there but it feels like it's blazing hot today and so you know going through the next couple years we'll be looking at uh you know developing some some uh you know pretty material security elements of the product as well so uh well on a path to a billion dollars is the net on that and uh you know we're we're fortunate to have have smb here and we're looking forward to introducing that to to those customers that have you know nfs workloads today with an s b component yeah nice tailwind good tam expansion strategy matt thanks so much we're out of time but really appreciate you coming on the program we appreciate you having us and uh thanks much dave good to see you all right good to see you and you're watching the convergence of file and object keep it right there we'll be back with more right after this short break [Music]

>> Welcome back to the program. My name is Dave Vellante and in this session we're going to explore one of the more interesting topics of the day. IoT for smart factories and with me are Todd Edmunds, the global CTO of Smart Manufacturing, Edge and Digital Twins, at Dell Technologies. That is such a cool title. (Todd laughs) I want to be you. And Dr. Aditi Banerjee, who's the Vice President General Manager for Aerospace Defense and Manufacturing at DXC Technology. Another really cool title. Folks, welcome to the program. Thanks for coming on. >> Thanks Dave. >> Thank you. Great to be here. >> Well- >> Nice to be here. >> Todd, let's start with you. We hear a lot about Industry 4.0, smart factories, IIoT. Can you briefly explain, like, what is Industry 4.0 all about and why is it important for the manufacturing industry? >> Yeah, sure Dave. You know, it's been around for quite a while and it's got, it's gone by multiple different names. As you said, Industry 4.0, smart manufacturing, industrial IoT, smart factory. But it all really means the same thing. It's really applying technology to get more out of the factories and the facilities that you have to do your manufacturing. So being much more efficient. Implementing really good sustainability initiatives. And so we really look at that by saying, "Okay, what are we going to do with technology to really accelerate what we've been doing for a long, long time"? So it's really not, it's not new. It's been around for a long time. What's new is that manufacturers are looking at this, not as a one-off, two off individual use case point of view, but instead they're saying, "We really need to look at this holistically, thinking about a strategic investment in how we do this." Not to just enable one or two use cases, but enable many, many use cases across the spectrum. I mean, there's tons of 'em out there. There's predictive maintenance and there's OEE, overall equipment effectiveness, and there's computer vision. And all of these things are starting to percolate down to the factory floor, but it needs to be done in a little bit different way. And really to to really get those outcomes that they're looking for in smart factory, or Industry 4.0, or however you want to call it. And truly transform. Not just throw an Industry 4.0 use case out there, but to do the digital transformation that's really necessary and to be able to stay relevant for the future. You know, I heard it once said that you have three options. Either you digitally transform and stay relevant for the future or you don't and fade into history like 52% of the companies that used to be on the Fortune 500 since 2000, right. And so really that's a key thing and we're seeing that really, really being adopted by manufacturers all across the globe. >> Yeah, so Aditi, that's like digital transformation is almost synonymous with business transformation. So is there anything you'd add to what Todd just said? >> Absolutely, though, I would really add that what really drives Industry 4.0 is the business transformation. What we are able to deliver in terms of improving the manufacturing KPIs and the KPIs for customer satisfaction, right. For example, improving the downtime, you know, or decreasing the maintenance cycle of the equipments or improving the quality of products, right. So I think these are lot of business outcomes that our customers are looking at while using Industry 4.0 and the technologies of Industry 4.0 to deliver these outcomes. >> So Aditi, one, if I could stay with you and maybe this is a bit esoteric, but when I first started researching IoT and Industrial IoT 4.0, et cetera, I felt, you know, while there could be some disruptions in the ecosystem, I kind of came to the conclusion that large manufacturing firms, aerospace defense companies, the firms building out critical infrastructure, actually had kind of an incumbent advantage and a great opportunity. Of course, then I saw on TV, somebody now, they're building homes with 3D printers. It like blows your mind. So that's pretty disruptive. But. So, but they got to continue, the incumbents have to continue to invest in the future. They're well capitalized. They're pretty good businesses. Very good businesses. But there's a lot of complexities involved in kind of connecting the old house to the new addition that's being built, if you will. Or there's transformation that we're talking about. So my question is how are your customers preparing for this new era? What are the key challenges that they're facing in the blockers, if you will? >> Yeah, I mean the customers are looking at Industry 4.0 for greenfield factories, right. That is where the investments are going directly into building the factories with the new technologies with the new connectivities, right, for the machines, for example. Industry IoT, Having the right type of data platforms to drive computational analytics and outcomes, as well as looking at edge versus cloud type of technologies, right. Those are all getting built in the greenfield factories. However, for the install-based factories, right, that is where our customers are looking at how do I modernize, right. These factories. How do I connect the existing machine? And that is where some of the challenges come in on, you know, the legacy system connectivity that they need to think about. Also, they need to start thinking about cybersecurity and operation technology security, right, because now you are connecting the factories to each other, right. So cybersecurity becomes top of mind, right. So there is definitely investment that is involved. Clients are creating roadmaps for digitizing and modernizing these factories and investments in a very strategic way, right. So perhaps they start with the innovation program. And then they look at the business case and they scale it up, right. >> Todd, I'm glad Aditi brought up security because if you think about the operations technology, you know folks, historically they air gapped, you know, the systems. That's how they created security. That's changed. The business came in and said, "Hey, we got to connect. We got to make it intelligent." So that's got to be a big challenge as well. >> It absolutely is Dave. And, you know, you can no longer just segment that because really to get all of those efficiencies that we talk about, that IOT and industrial IoT and Industry 4.0 promise, you have to get data out of the factory but then you got to put data back in the factory. So no longer is it just firewalling everything is really the answer. So you really have to have a comprehensive approach to security, but you also have to have a comprehensive approach to the cloud and what that means. And does it mean a continuum of cloud all the way down to the edge, right down to the factory? It absolutely does because no one approach has the answer to everything. The more you go to the cloud, the broader the attack surface is. So what we're seeing is a lot of our customers approaching this from, kind of, that hybrid, you know, write once, run anywhere on the factory floor down to the edge. And one of things we're seeing too is to help distinguish between what is the edge and that. And bridge that gap between, like Dave, you talked about IT and OT, and also help that what Aditi talked about is the greenfield plants versus the brownfield plants, that they call it, that are the legacy ones and modernizing those, is it's great to kind of start to delineate. What does that mean? Where's the edge? Where's the IT and the OT? We see that from a couple of different ways. We start to think about, really, two edges in a manufacturing floor. We talk about an industrial edge that sits, or some people call it a far edge or a thin edge, sits way down on that plant. Consists of industrial hardened devices that do that connectivity, the hard stuff, about how do I connect to this obsolete legacy protocol and what do I do with it? And create that next generation of data that has context. And then we see another edge evolving above that which is much more of a data and analytics and enterprise grade application layer that sits down in the factory itself that helps figure out where we're going to run this. Is... Does it connect to the cloud? Do we run applications on-prem? Because a lot of times that on-prem application is needs to be done because that's the only way it's going to work. Because of security requirements. Because of latency requirements, performance, and a lot of times, cost. It's really helpful to build that multiple edge strategy because then you consolidate all of those resources, applications, infrastructure, hardware, into a centralized location. Makes it much, much easier to really deploy and manage that security. But it also makes it easier to deploy new applications, new use cases, and become the foundation for DXC's expertise in applications that they deliver to our customers as well. >> Todd, how complex are these projects? I mean, I feel like it's kind of the digital equivalent of building the Hoover Dam. I mean, it... So, yeah, how long does a typical project take? I know it varies, but what, you know, what are the critical success factors in terms of delivering business value quickly? >> Yeah, that's a great question in that we're, you know, like I said at the beginning, this is not new smart factory and Industry 4.0 is not new. It's been... It's people have been trying to implement the holy grail of smart factory for a long time. And what we're seeing is a switch, a little bit of a switch or quite a bit of a switch, to where the enterprise and the IT folks are having a much bigger say and have a lot to offer to be able to help that complexity. So instead of deploying a computer here and a gateway there and a server there. I mean, you go walk into any manufacturing plant and you can see servers sitting underneath someone's desk or a PC in a closet somewhere running a a critical production application. So we're seeing the enterprise have a much bigger say at the table. Much louder voice at the table to say, "We've been doing this enterprise all the time. We know how to really consolidate, bring hyper-converged applications, hyper-converged infrastructure, to really accelerate these kind of applications. Really accelerate the outcomes that are needed to really drive that smart factory." And start to bring that same capabilities down into the Mac on the factory floor. That way, if you do it once to make it easier to implement you can repeat that. You can scale that. You can manage it much easily. And you can then bring that all together because you have the security in one centralized location. So we're seeing manufacturers... Yeah, that first use case may be fairly difficult to implement and we got to go down in and see exactly what their problems are. But when the infrastructure is done the correct way, when that... Think about how you're going to run that and how are you going to optimize the engineering. Well, let's take that what you've done in that one factory and then set. Let's that, make that across all the factories including the factory that we're in, but across the globe. That makes it much, much easier. You really do the hard work once and then repeat almost like a cookie cutter. >> Got it, thank you. Aditi, what about the skillsets available to apply these to these projects? You got to have knowledge of digital, AI, data, integration. Is there a talent shortage to get all this stuff done? >> Yeah, I mean, definitely. Different types of skillsets are needed from a traditional manufacturing skillset, right. Of course, the basic knowledge of manufacturing is important. But the digital skillsets, like, you know, IoT. Having a skillset in different protocols for connecting the machines, right. That experience that comes with it. Data and analytics, security, augmented virtual reality, programming. You know, again, looking at robotics and the digital twin. So, you know, it's a lot more connectivity software data-driven skillsets that are needed to smart factory to life at scale. And, you know, lots of firms are, you know, recruiting these types of resources with these skillsets to, you know, accelerate their smart factory implementation as well as consulting firms like DXC technology and others. We recruit. We train our talent to provide these services. >> Got it. Aditi, I wonder if we could stay on you. Let's talk about the partnership between DXC and Dell. What are you doing specifically to simplify the move to industry 4.0 for customers? What solutions are you offering? How are you working together, Dell and DXC, to bring these to market? >> Yeah, I... Dell and DXC have a very strong partnership, you know, and we work very closely together to create solutions, to create strategies, and how we are going to jointly help our clients, right. So. Areas that we have worked closely together is edge compute, right. How that impacts the smart factory. So we have worked pretty closely in that area. We're also looked at vision technologies, you know. How do we use that at the edge to improve the quality of products, right. So we have several areas that we collaborate in and our approach is that we want to bring solutions to our client and as well as help them scale those solutions with the right infrastructure, the right talent, and the right level of security. So we bring a comprehensive solution to our clients. >> So, Todd, last question. Kind of similar but different. You know, why Dell DXC? Pitch me. What's different about this partnership? You know, where are you confident that, you know, you're going to deliver the best value to customers? >> Absolutely, great question. You know, there's no shortage of bespoke solutions that are out there. There's hundreds of people that can come in and do individual use cases and do these things and just... And that's where it ends. What Dell and DXC Technology together bring to the table is we do the optimization of the engineering of those previously bespoke solutions upfront, together. Right. The power of our scalables, enterprise grade, structured, you know, industry standard infrastructure as well as our expertise in delivering package solutions that really accelerate with DXC's expertise and reputation as a global trusted advisor. Be able to really scale and repeat those solutions that DXC is so really, really good at. And Dell's infrastructure and our, what, 30,000 people across the globe that are really, really good at that scalable infrastructure to be able to repeat. And then it really lessens the risk that our customers have and really accelerates those solutions. So it's, again, not just one individual solutions. It's all of the solutions that not just drive use cases but drive outcomes with those solutions. >> Yeah, you're right. The partnership has gone... I mean, I first encountered it back in, I think, it was 2010, May of 2010. We had you guys both on the queue... I think we were talking about converged infrastructure and I had a customer on, and it was actually manufacturing customer. Was quite interesting. And back then it was how do we kind of replicate what's coming in the cloud? And you guys have obviously taken it into the digital world. Really want to thank you for your time today. Great conversation. And love to have you back. >> Thank you so much. >> Absolutely. >> It was a pleasure speaking with you. >> I agree. >> All right, keep it right there for more discussions that educate and inspire on theCUBE.

(electronica music) >> Hello everybody. This is Dave Volante and we are covering HPE's big GreenLake announcements. We've got wall-to-wall coverage, a ton of content. We've been watching GreenLake since the beginning. And of one of the things we said early on was let's watch and see how frequently, what the cadence of innovations that HPE brings to the market. Because that's what a cloud company does. So, we're here to welcome you. Keith White is here as the Senior Vice President General Manager of GreenLake cloud services. He runs the commercial business. Keith, thanks for coming on. Help me kick off. >> Thanks for having me. It's awesome to be here. >> So you guys got some momentum orders, 40% growth a year to year on year. You got a lot of momentum, customer growth. >> Yeah, it's fantastic. It's 46%. >> Kyle, thank you for that clarification. And in 46. Big different from 40 to 46. >> No, I think what we're seeing is we're seeing the momentum happen in the marketplace, right? We have a scenario where we're bringing the cloud experience to the customer on their premises. They get to have it automated. Self-serve, easy to consume. They pay for what they use. They can have it in their data center. They can have it at the edge. They can have it at the colo, and, we can manage it all for them. And so they're really getting that true cloud experience and we're seeing it manifest itself in a variety of different customer scenarios. You know, we talked about at Discover, a lot of work that we're doing on the hybrid cloud side of the house, and a lot of work that we're doing on the edge side of things with our partners. But you know, it's exciting to see the explosion of data and how now we're providing this data capability for our customers. >> What are the big trends you're hearing from customers? And how is that informing what you're doing with Green? I mean, I feel like in a lot of ways, Keith, what happened last year, you guys were, were in a better position maybe than most. But what are you hearing and how is that informing your go forward? >> Yeah, I think it's really three things with customers, right? First off, Hey, we're trying to accelerate our digital transformation and it's all becoming about the data. So help us monetize the data, help us protect that data. Help us analyze it to make decisions. And so, you know, number one, it's all about data. Number two is wow, this pandemic, you know, we need to look for cost savings. So, we still need to move our business forward. We've got to accelerate our business, but help me find some cost savings with respect to what I can do. And third, what we're hearing is, hey, we're in a situation, where there's a lot of different capabilities happening with our workforce. They're working from home. They're working hybrid. Help us make sure that we can stay connected to those folks, but also in a secure way, making sure that they have all the tools and resources they need. So those are sort of three of the big themes that we're seeing that GreenLake really helps manifest itself, with the data we're doing now. With all the hybrid cloud capabilities. With the cost savings that we get with respect to our platform, as well as with solutions such as VDI or workforce enablements that we've, we create from a solution standpoint. . >> So, what's the customer reaction, I mean, I mean, everybody now, who's has a big on-premise state, has an as a service capability. A customer saying, oh yeah, oh yeah, how do you make it not me too? In the customer conversations? >> Yeah. I think it turns into, you know, you have to bring the holistic solution to the customer. So yes, there's technology there and we're hearing from, you know, some of the competitors out there. Yeah, we're doing as a service as well, but maybe it's a little bit of storage here. Maybe it's a little bit of networking there. Customers need that end to end solution. And so as you've seen us announce over time, we've got the building blocks, of course, compute storage and networking, but everything runs in a virtual machine. Everything runs in a container or everything runs on the bare metal itself. And that package that we've created for customers means that they can do whatever solution, or whatever workload they want So, if you're a hospital and you're running Epic for your electronic medical records, you can go that route. If you're upgrading SAP and you're using virtual machines at a very large scale, you can use this, use a GreenLake for that as well. So, as you go down the list, there's just so many opportunities with respect to bring those solutions to our customers. And then you bring in our point-next capabilities to support that. You bring in our advisory and professional services, along with our ecosystem to help enable that. You bring in our HPE financial services to help fund that digital transformation. And you've got the complete package. And that's why customers are saying, hey, you guys are now partners of us. You're not just a hardware provider, you're a partner you're helping us solve our business problems and helping us accelerate our business. >> So what should people expect today? You guys got some announcements. What should people look for? >> Well, I think this is, as we've talked about, you know, now we're sort of providing much more capabilities around the data side of the house. Because data is so such, it's the gold, if you will, of a customer's environment. So first off we want to do analytics. So we want an open platform that provides really a unified set of analytics capabilities. And this is where we have a real strong, sweet spot with respect to some of the, the software that we've built around Esperal. But also with the hardware capabilities. As you know, we have all the way up to the Cray supercomputers that, that are doing all of the analytics for whether this or, or financial data that. So, I think that's one of the key things. The second is you got to protect that data. And, and so if it's going to be on prem, I want to know that it's protected and secured. So how do I back it up? How do I have a disaster recovery plan? How do I watch out for ransomware attacks, as well? So we're providing some capabilities there. And then I'd say, lastly, because of all the experience we have with our customers now implementing these hybrid solutions, they're saying, hey, help me with this edge to cloud framework and how do I go and implement that on my own? And so we've taken all the experience and we've bucketed that into our edge to cloud adoption framework to provide that capability for our customers. So we, you know, we're really excited about, again, talking about solutions, talking about accelerating your business, not just talking about technology. >> I said up the top, Keith, that one of the ways I was evaluating you as the pace and the cadence of the innovations. And, and is that, is that fair? How do you guys think about that internally? Are you, you know, you're pushing yourself to go faster, I'm sure you are, but what's that conversation like? >> I think it's a great question because in essence, we're now pivoting the company holistically to being a cloud services and a software company. And that's really exciting and we're seeing that happen internally. But this pace of innovation is really built on what customers are asking us for us. So now that we've grown over 1200 customers worldwide. You know, over $5 billion of total contract value. You know, signing some, some large deals in a variety of solutions and workloads and verticals, et cetera. What we're now seeing is, hey, this is what we need. Help me with my internal IT out to my business groups. Help me with my edge strategy as I build the factory of the future, or, you know, help me with my data and analytics that I'm trying to accomplish for my, you know, diagnosis of, of x-rays and, and capabilities such as Carestream, if you will. So it's, it's exciting to see them come to us and say, this is the capabilities that we're requiring, and we've got our foot on the gas to provide that innovation. And we're miles ahead of the competition. >> All right, we've got an exciting day ahead. We got all kinds of technology discussions, solution discussions. We got, we got, we're going to hear from the analyst community. Really bringing you the, the full package of announcements here. Keith, thanks for helping me set this up. >> Always. Yeah. Thanks so much for having me. >> I look forward today. And thank you for watching. Keep it right there. Tons of content coming your way. You're watching The Cubes coverage of HP's big GreenLake announcement. Right back. (electronica music)

>> From Yorktown Heights, New York, it's theCUBE, covering IBM Cloud Innovation Day, brought to you by IBM. >> I'm Peter Burris of Wikibon. Welcome back to IBM Innovation Day, covered by theCUBE, from beautiful Yorktown Heights, New York, Thomas J. Watson Research Center. A lot of great conversations about the journey to the cloud and what it means, and we're going to have another one here with Denis Kennelly, who is the General Manager of Cloud Integration in IBM. Denis, welcome to theCUBE. >> Thank you, Peter, and welcome to Yorktown also. >> I love it here. So, very quickly, what does the GM of Cloud Integration do? >> Yeah, so, I suppose we start from the beginning, right? So I am responsible for a lot of what we call the traditional IBM middleware. So these are brands that are known to the industry and to our customers, things like WebSphere, Message Queue, or MQ, as we know it, which is kind of the core foundation stones for a lot of IT today that's out there in the industry. And it's not just about, you know, sometimes people talk about this legacy, but this is what all the systems run on today. And also, I'm involved in the whole journey of moving that middleware to the cloud and enabling customers to get on that journey to cloud. And it's not just to a cloud, because your typical enterprise today has probably on average about five different clouds, and clouds, as we know them as the IS players of the past, but also when we talk about cloud, we also think about things like SaaS properties and applications of that regard. So it's helping customers go from that traditional IT infrastructure and on their journey to the cloud. That's what I do. >> So utilizing these enterprise-ready technologies that have driven the enterprise, bringing them to the cloud as services, but also making sure that the stuff that's currently installed can engage and integrate the cloud from a management service standpoint as well. >> Absolutely, because customers have made a huge investment in this middleware, and a lot of the transactions, and a lot of the security, and a lot of the risks set in these systems, and they have served us very well for many decades. Now, as we start to move to the cloud, it isn't a binary switch. It's going to be a transition over time, and today, I think we're about 20% into that journey. I would say we've done some of the easier parts. Now we're getting into some of the more complex and some of the more difficult problems. And kind of one of the underlying pieces of technology we're using to enable customers to do that is container technology. So we've made the decision to use containers right across our middleware, our software. So what I mean by that is we've taken all our software and it's running on containers today, and that's a key enabler to make this happen, because containers give you that flexibility and that openness to run on different targeted environments and be able to run on different clouds at the end of the day. >> The model by which developers thought about integration would be through a transaction. Generally pretty stateful. So, I'll put something in a queue, I'll wait for a response, guaranteed delivery. Now we're moving to a world, containers, a lot more reliance on stateless interactions. It means we're being driven mainly by events. I'm thinking in terms of events. Talk about how that is changing the way we think about the role of middleware or the role of integration amongst all these different possible services. >> Yeah, it's a great point. I mean, so if you think about containers, we think about stateless, and we think about microservices, and we talk about event-based applications, so a lot of those front ends are on that today and building on those technologies. So you've got to enable the new developers to build in that way. Now, how do you integrate that with that backend, right? Because at the end of the day, these transactions are running in the backend, and you really want to enable, as part of the transformation, you want to open up those backends to those new developers and to those new customer insights, because what is digital transformation? It's about putting the customer at the middle and enable insights on those customers, and enable rapid development of those applications. So at the core of that is integration, and integration is not just message-based integration. It's being able to take those backend transactions and surface them up through APIs, not just the standard APIs as we think of maybe as web services, but event-based probability models, and event-based APIs also, and doing that in a consistent and a secure manner, because if you have all these complex transactional systems, who has access to that data? Who has access to make those transactions? Who can, at certain levels, et cetera, and we really have to do that in a secure and a consistent manner across these environments is critical to what we do. >> So, can you give us some examples of some customers that are successfully transitioning their backend systems to these new technologies in a way that protects the backend system, makes it economical to do so, in other words, doesn't force change, but can utilize some of these new integration technologies to make both the new investments more valuable but also the backends more valuable too. >> Yeah, I mean, if you think of, I'll give you an example of a customer, American Airlines, in the airline industry, right? So, if you think about travel and airline travel in times past, you know, you made a reservation maybe through an agent and you booked the flight from A to B. Today, you have your cellphone, you get regular updates on your flights. If you're delayed, you're possibly offered re-routing options, et cetera, right, so there's a classic example of how digital has transformed the airline industry and the airline booking industry. If your flight, you know, if there's weather patterns, et cetera, how you can get real time updates on your flights. So, okay, that's all happening on the front end, on your cellphone, or your tablet, or whatever, but the backend booking system is still a transactional-based system that says, Peter is on this flight going from A to B at this time, et cetera. So, that's an example of how we have modernized an application and we have worked with American Airlines to make that happen, to give you that kind of 360 view as a customer, where you bring in together flight information, weather information, rating information, because we'll offer you different alternatives in terms of if you need to rebook in the event of something going on, and at the backend, there's still a transaction that says, book Peter on this flight from A to B, and that's a real life example of a transformation, how we've integrated those two worlds there. >> So if we go back five or six, or more than that, say 10, 15 years, in the days of MQ, for example, the people who were developing, and setting up those systems, and administering and managing those systems were a relatively specialized group. Today, the whole concept of DevOps in many respects is borrowing from much of the stuff that those folks did many, many years ago as infrastructure builders, or developers, as I call them. How does that group move into this new world of integration in the cloud? >> Yeah, so, I think first of all, the rate and pace has multiplied, right, so the rate and pace of which we make changes to the system has multiplied. I mean, maybe traditionally, we run in changes maybe once a month. We have things like change control windows. Things were very well controlled, et cetera, right? But at the end of the day, it doesn't meet the needs of today and what we need to do in a digital world. So today, we're running in changes on the hour. So now, you're faced with a challenge, right? So when you make changes, how do you know that the system is still performing, is still operating at the level you need it to operate on? You start to think about security and you start to think about, okay, I've made a change, have I introduced vulnerabilities into the system? You've got to, you know, in the past, these were all separate groups and almost islands within the operation center, where you have the developer, who kind of over to all the code, and then operations looked at it and see how it's performed, and security checked for compliance, et cetera, and they were kind of three different islands of personas or groups within the organization. Today, that's really collapsing into one organization. The developer is responsible for making sure the change gets in, for making sure the change performs, and is also security compliant. And we call this the role of the SRE, or the systems reliability engineer, and really bringing those two worlds together into one persona, and it's not just one persona but having the systems on the inside to make that happen. And that's critical in how management is changing and the management of these systems is changing, and how the skill level is needed in this new world. >> So Denis, one more question. In a few months, IBM Think is going to take over San Francisco, February 2019, >> Looking forward to it. >> 3,000 people. Talk to us a little bit about what gets you excited about Think, and what kind of conversations you hope to be having while you're there. >> Yeah, well, you know, this is the one time of the year where all of IBM comes together, and it's new this year that we're going to San Francisco, and in particular, in our cloud business, which I'll talk about, which really encompasses everything we're talking about here, which is our middleware business and also how we move customers to the cloud, and really engaging with customers in those conversations. And this is the one time of the year where all of IBM comes together, and where you can see the full breadth of our capabilities all the ways from our systems, and the hardware, down at that level, at the chip level, right through to the middleware and the software to our cloud, and actually engaging with customers, and really understanding what the customer needs are, and making sure that what we are working on is meeting those customer needs, and of course, if we need to adapt or change, and take that feedback back into the organization, so we do that in real time. It's a very exciting time for us. It's a week in the year that I really look forward to, because that's where all of IBM comes together, including our services, et cetera, and where we actually have conversations with key customers and partners and really understanding what's going on in the industry and how we can help people on this journey to the cloud that I talked about. >> Denis Kennelly, IBM General Manager of Cloud Integration, thanks very much for being on theCUBE. >> Thank you, Peter. And once again, this is Peter Burris. We're signing off from the IBM Innovation Day, here at the Thomas J. Watson Research Center in Yorktown Heights. Thank you very much for watching. Let's carry on these conversations about cloud and the future of computing.

>> (dramatic orchestral music) >> Announcer: Live from San Francisco. It's the Cube. Covering Red Hat Summit 2018. Brought to you by Red Hat. >> Hello welcome back everyone. Day three of wall-to-wall coverage here at Red Hat Summit 2018 live in San Francisco, California, here at Moscone West. I'm John Furrier, your co-host of The Cube with John Troyer, analyst, co-host this week. He's the co-founder of TechReckoning, and advisory and community development firm. Our next guest is our (mumble) of the senior Vice President General Manager of Hewlett Packard Enterprises Pointnext HPE. Great to see you. >> Great to see you as well. Thank you. >> So there's not secret HPE been partnering with companies for many generations. And Red Hat is one of the big strategic partners. Lot of services opportunity, a lot of transformation happening, and the biggest thing is that true Private Cloud and Hybrid Cloud, and Public Clouds all happening an IOT Edge is kind of seeing pretty clearly what's happening. On-Premise isn't going away. >> No! >> It'll look like Cloud is going to run like a Cloud. >> Yeah. >> Has to work with the Cloud or Clouds plural, and then you got the IOT Edge out there-- >> That's right. >> All kind of coming together with software Kubernetes containers all kind of being glue layers in here. So, you know, must be good for you guys okay, customers can now see what you guys have been promoting. So what is HP doing with their ad? How's that tie into that-- >> Sure, sure >> You know, transformation with the cloud? >> You said it very well John. In fact when we talked to our customers weather they realized it or not, it's the Hybrid world, and the environments are hybrid, and like you said, probably private (mumble) are not going anywhere. In fact we did the CTPF acquisition, Red Pexia acquisition, and this is really all to help clients on the Cloud journey. Doesn't really matter to us whether the workload ends up in AWS, Google, Azure, on Prime or dedicated infrastructure. So, that's actually been a huge plus for us to really have a seat at the table, to have a discussion on the customers workload strategy. Now a partner like Red Hat, who have been together working together for probably 18 years now, and it's been a long steady partnership. Who they're number one OAM partner but also the point you made I think from a services standpoint that's just a huge opportunity you know, customers tell us anyone can do infrastructure service or they're looking for platforming service. So in jointly with our consumption capabilities, and Red Hat Open Shift. Now who giving them true Container Product Service. >> Containerization, how we were talking yesterday in our wrap-up. You can bring in the new without killing the old and but it's really fundamental because people want Cloud scale, they want the horizontal scalable application, devops and programing infrastructures code. But they can't just throw out their legacy stuff. Containers which allows them to nurture those applications and workload, and let it take it's natural course. This is actually good for services cause you can take-- there's a solution there. >> That's right! There's absolutely. In fact customers tell us when they looking for the platform, it's not just to help them on their new build. They're looking for help also to run the existing environment and most of the times it's not practical to re-factor, re-architect every single of the Legacy applications, and cause some of them applications, as you know, they were done to leverage the performance optimization on the underlying infrastructure piece of it, and so one of the things we're doing join to the Red Hat is leverage Containerization to provide the portability for the applications. To move between the different environments and whether it's Private Cloud, Public Cloud, but the key thing is portability, and mobility and that's sweet spot for containerization. >> Give some use cases of customers. Take us through a day-in-the-life of maybe a couple different examples where you guys are engaging with Red Hat where you coming in the customer is like, "Okay, here's my situation". What are some of the trends and patterns that you see with customers? What specifically are you, is it workload, moving it to the mobile clouds? Is it more re-platforming On-Premise. >> Yeah! >> What are some of the things that you guys are doing? >> I would say that the bulk of our engagement, and that's one thing that we feel really good about joining Red Hat. We have really shifted our engagement model to be much more outcome driven. So the discussions with the client is always start off with like a workshop, and within that workshop we're actually understanding where the customer is really trying to go, what business outcomes they're trying to achieve? Before we start we going to push a specific technology or stack with specific solution set, and by having that alignment, in in fact, we talk about that IT means to be embedded with the business. Not alignment, embedded with the business, and because the role of IT has changed. So when we talk about workload, right, it's about no longer, and I talked about this earlier today, you no longer running workload just within the Forward Data Center, and the traditional view of that IT owns and operates the Forward Data Center, that's just dead. So, it's really more about managing the supply chain. We talk about the overall workload strategy. Which workloads make the most sense to go on Public Cloud, Private Cloud, and then the discussion also centers around their application portfolio and really understanding which applications truly need to be Cloud Native. Which ones really need to be left in shift, and this whole portability concept comes into play and that's one thing joining with Red Hat because Red Hat is really good joining with us on driving this kind of innovation workshops. Then you heard this earlier today as well, and that's just the fun of if. When no longer you talking about PowerPoint presentation, this and that. It's getting in a room, getting on a White Board and talking about what kind of journey really make sense for that party-- >> That's been really notable here, this week at this conference, right. There a lot of tech, a lot of software talked about, but also on the keynote a lot of people talking about culture, transformation, getting beyond your process, and the places you get stuck as IT professionals. So that's a great way to approach it. Right, nobody starts with a list of skews-- >> No! And absolutely, the other point is that one of the things that always gets missed is the focus on the management of change, and that's one of the key pieces we emphasize that not just the business process, but the culture, the people. How you going to bring them along the change journey. So, we actually put lot of emphasis on the whole area around management of change. We actually have a practice that this is one of the keys areas they focus on. So, you're absolutely right. Key focus area. >> I did want to flip to the products for a second. There was an announcement here now and talk a little bit about HP Synergy, Composable Infrastructure, with Open Shift. Maybe if you have a headline on exactly how you guys describe Synergy and then maybe how we working with Open Shift. >> So the HP Synergy the best way I can describe it is it is truly industry first composable infrastructure, and it gives you the ability to pull fluid resources and with software intelligence built in, and Unified API. It really gives you the ability to pull the resource that you need for specific applications. In fact, I use the analogy, it's kind of like building Legos and you can pull together based on what you going to do at a given moment, and then you decompose it and build something new. So it's all done via a software and truly gives you that flexibility that customers have been seeking. So it's just to me its got a great market traction across the globe and we'll just see continued momentum when joining with the Red Hat. What we've done is now with the announcing new solutions like the one you referenced to, to support ansible automation of the Red Hat Open Shift on the Synergy platform from the three part and the Nimble product lines and it just helps scale the Open Shift and while making container operation simple, scalable and more importantly repeatable. >> I want to make sure that I get this out there, because you guys were early with composable. Dave Valata and I had a debate on this at one of your HP Discovers where, I was really lov'n the composable message. Although it was kind of for a different massage but at that time Devos was really picking up steam. But, it's actually happening now three years later the level of granularity to services level as microservices as it comes the architecture of the future. The services model is literally, "What do you want?" it's not, "Here's the solution", it's like< "What do you need?" so, you're buying off the menu, if you will, so that changes the game. So congratulations on having that composable method first. I got to ask you, the impact to the engagements. So you now have menu of services. Does that change how you guys go to market? You mention that you do kick of meeting, you do the needs assessment, so I get that. Check! good approach. But the customers now, they just want to make sure that it's custom for them. How does that change your engagement? >> At the CXO level, the discussion, no mater which way you start the discussion it tends to kind of follow into a few buckets. Rather it's about generating additional revenue, going to market quicker, or it's about safe to invest, reducing their operating expenses, or it's about securing their information network. One of the thing we find is especially if you take a look at even the containers, applications deploying it. It's one thing to deploy in the corporate environment but if you're trying to scale that with an enterprise. If the enterprises look for added features for their security, whether it's persistent storage and again the focus always turns into what can you do to help drive the total cost of ownership down. I think with Red Hat this is one thing that works great with Open standards. The focus is really much more around not just the simplicity, reducing costs, it's also about improving performance. Rather it's the physical virtual environment. So, you're right, the menu of services. Whether it's you talking about IOT Use Scape and I think you going to see more and more of that with the user experience, the focus that we talked about. Context of our apps. I use the example of going to the airport, getting into whatever transportation you using these days, but the point from point A to point B, you're no longer fumbling through cash or credit cards. It's a very easy experience, much more personalized much more usable and a lot of what some of the hospitality franchises are doing, whether you look at Starwood Properties, Marriott. Now you use a mobile device to access your room, and as soon as you get into some of the hotel property, as soon as you access their Wifi coverage all of a sudden you can actually, the hotel property picks you up. They can provide you with the navigation, how to get to your room and depending on your profile, and whether you opted in or opted out, they will push and their partners will push some specific services to you. So, how you are able to create that kind of experience and drive additional revenue and all that is possible to the point he just make, it's truly a flourishing eco-system of micro services and apps driven by the-- >> I think that business now seeing that which is great about that having a clear line of site that these new apps and new experiences is going to drive top line revenue for your customers. I got to ask you about the services now. With more services comes more delivery, right? So, options, ecosystems, you guys have a pretty big ecosystem right as a lot of other providers. You guys always worked will with multiple companies. How are you guys engaging with Pointnext with now new sets of service providers and your network. You got Cloud Service and you have someone actually maybe could be an intergrater, could be a software developer. How do you deal with this new stake holder in your equation? >> After all the spin mergers have been completed now and I think after DXC1 it really open up the door to get a lot of the system (mumble) back on the table because they don't really view us as competitor anymore. Because we no longer have a large the EDS acquisition that we had now the DXE. So whether you look at Accenture or whether you look at Deloitte and the other (mumble) we're actually partnering with them very well both in joint submission creation but also when we talk about true additions transformation for our client a lot of expertise they bring to us is very complimentary to what we have. So one of the thing we do very well is really around the technology advisor services. (mumble) bring more of the business advisory services as well as the specific vertical depth around the specific vertical whether it's emphasized retail. So when somebody talking about retail of the future or something like that. You marry the two together and you have a strong value proposition. I think the area that we have to put a lot more emphasis upon is more around program management, and because now you actually are trying to show that one outcome for the client, so it's very important whether you working with the ISB or whet ever you working with DSI or whether you working with the other intergraters, and your own resources how you going to bring that pool together around specific tracks and deliver a one common objective for the clients? The Program Manager plays a huge role in this process. >> For the folks watching. What should they know about HP Pointnext that they many or may not know about or should know about that that highlights what you guys are doing. Can you simplify, what is the value proposition that Pointnext is bring to customers? >> As the brand itself states, the Pointnext, it's really about working with the clients finding what's next in their journey. One of the thing I would say and a lot of people get surprised by this, even with after all the spin merge. We are twenty-five thousand people plus strong and we have a lot of great and deep appreciation when it comes to some of these solution and one thing we do very well is partner. Whether it's Red Hat and other SI and bring some unique innovative solution to the market and one of the thing Jim talked about here is all about accelerating user driven innovation, and when you take a look at some of the use cases we're rolling out and I talked about the analytics and the one AI project and how we're helping manufacturing clients or other use cases to truly analyze patterns and predict failures and increase productivity. These discussions customers truly trust us. With the (mumble) and CTP acquisitions we no longer just having On-Premise discussions. We have a strong public hard knowledge. It doesn't matter whether you cloud journey involves AWS, Google, Azure and what not. We are able to actually provide a very objective road map for the workload strategy and the transmission journey. >> The users in the communities as Jim pointed out in the meeting yesterday. The communities in Open Source are now also your customers. >> Right. >> So your customers are also participating in these projects upstream. Are you guys doing an Open Source work? What Pointnext doing? Are you guys relying on that community? Is there a crossover between your customers and those users in the Open Source community? >> Yeah, we always had a very strong (mumble) with the Open Source community. We contributed a lot to the Open Source communities and if you take a look at now as we working with the number of this next generation of partners, whether it's darker, scale it and Red Hat and others it's truly opened up the boundaries as to what can we push to drive new kind of solution there. I love what some of the speakers said yesterday. You remember the example from the Boston Children's Hospital where they talked about they didn't want to deal with the complexity, they'd rather focus on what they do best and so one of the thing we're focused on in the Open Source Continuity is the driving more standardization and automation. So you can run applications as scale. You can run analytics as scale. I think those are somethings we can bring to the table. >> Great! You know the thing about what's going on now with these abstraction layers is an opportunity to create new services and accelerate the services, and congratulations. Great to have you on the program. Thanks for sharing the update. >> Absolutely! >> Congratulation on your deep partnership with Red Hat. Go to see HP Pointnext doing well. Thanks for coming on. >> Thank you so much. >> Live coverage here in San Francisco California. Red Hat Summit 2018 will continue. I'm John Furrier John Troyer. Stay with us more coverage after this short break. >> (electronic music) >> Often times a communities all ready know about facilities that are problematic, because they smell it, they see it but

live from San Francisco it's the cube covering Red Hat summit 2018 brought to you by Red Hat welcome back everyone we're here live in San Francisco for the Red Hat summit 2018 events the cubes exclusive coverage I'm John for the coast of the Q with John Troy you're my Coast analyst as we blues co-founder of tech reckoning advisory and Community Development firm meniscus is ranked at Rangachari vice president general manager Red Hat storage for you to see you again welcome back to the cube thank you thank you invited me again so Steve a lot they said storage is where all the action is with wellness data to be stored somewhere with the cloud yeah it's still important you guys have a new concept yeah on storage yeah I'm storage what is the unstirred you know I think essentially when we got into the storage business the status quo was your traditional storage mainframe so wheeling a piece of gear and it's to scale up and have things workloads running there but with the movement towards cloud especially with hybrid cloud where you really can't take a physical box and move it into a public cloud and in the last year or so with containers the common theme that's emerging is things like agility things like scale things like almost having ubiquitous storage all around the place is becoming more and more important so our thought is it almost turns a storage the phenomenal storage industry upside down on its head because the things that people cared about decade ago on the workloads are no longer relevant or less relevant than where they are today so and you know it seems to be people seem to get it so we're pretty I mean we've seen the strain on servers server less storage less so in a way this the recent resource pool is not just you know a box and provision the LUNs more like okay I need storage exact a button I don't care where it comes from is that we're kind of getting to somebody that's exactly what it is right I think in a in a different way right one of the customers said I want storage to be everywhere but nowhere right in that they want storage to be a pervasive but it has to be invisible so they don't have to worry about things like zoning and LAN masking on one piece hardware and do the same thing with twenty other pieces what our solution offers offers is truly a scalable storage platform that's running on any kind of footprint physical virtual private a public cloud but it's a common user experience across all these different footprints and that's why and the other part of this thing which is also different is yes it does appeal to the storage admins but more importantly as you become as organizations of all the cloud architects and DevOps know what they care about is like I want storage to be as invisible as possible but yet I want to make the devil developers more and more productive so I think we are I feel really right track in appealing to how storage needs to be viewed it's a no brainer in my mind if you're DevOps you want to go total cloud horizontally scalable you need gel apps stores just to be available programmable all that's great stuff question I have for you is what's the impact of customers who have been buying boxes for decades so what's the impact of them with Red Hat so I'm still gonna need boxes and still gonna put them somewhere and so it's an on-prem cloud operation so I still need storage bits cloud obviously those guys have their own storage but I mean but you still gotta plug it in and put storage in sure what's the impact of the customer well I think they I mean we do we are practical enough and we rarely realize that no customer is gonna pull the plug one day and move on to the next infrastructure what we are seeing more and more is as those new workloads which are dramatically different the previous workloads as they come into play then they have to rethink how they develop deploy provisioned storage infrastructure so that's where we come in so it's not about in either/or it's about how do you are meant your existing storage infrastructure but think about it in a modern way think about how you can future-proof your architectures so that it scales so that's the way we think about it wrong how should people be thinking about storage at different levels of the architecture there's actually a lot of storage here there's been a lot of sessions and the ecosystem expo there's a lot of storage providers but you've got the we've been talking a lot about open shift and an open shift on OpenStack here at the show this is this year so if you're at the OpenStack lair versus on the open shift layer how should you be thinking about storage and and what and what products are plugging in at those layers yes so you know think with OpenShift a couple of days ago earlier this week we announced our 150 customers we're actually deploying our product which is called containing nearly storage CNS for short and what that does is it enables it's essentially the storage infrastructure for open shaft so wherever open ship goes the storage footprint follows along whether you're running it on Prem on top of virtualized infrastructure or you're running any of these public clouds and the most interesting part of that is you know getting back to the earlier conversation we try to make it as invisible as possible so you we as vendors don't have to say you've got to deploy it here so make it as invisible as possible and as seamless as possible now with OpenStack it's a different set of experience because that's kind of infrastructure up right and the advantage for us is if you look at the OpenStack community in general almost 70% of the OpenStack community in one way shape or form uses a safe project so it's almost become I wouldn't call it almost de facto standard on how people manage the storage infrastructure with an open stack but even there the cardinal rules are still the same which is when they think about spinning up a machine the storage has to be attached automatically to it and then scale as their computer in the storage infrastructure scales and this the scale is the question we're living in a new era of cloud economics scale is key and we here the customers here Red Hat Red Hat's customers talking about things like horizontally scalable asynchronous micro sets of micro services levels of granularity this is the programmable new fabric that is a new infrastructure of the Internet you know 30 year old statuses from e-commerce DNS they're you know gonna be abstracted away with a new abstraction layer yes hello opens yes hello you know new things kubernetes and in containers so with that being said there's an opportunity yes so when you that's kind of like the state of the yard now but you're welcome to an enterprise like what's kubernetes again so you got some enterprises are learning about kubernetes and it's good news for them learning about containers where they don't have to throw away anything you just containerize it how is that impacting the classic definition of software-defined datacenter yeah and software-defined storage because those are the two important trends that have been happening in Software Defined does it accelerate it does it change it a little bit what's your thoughts on those do you know I think it accelerates it and here's why that's a great question right because when you look at organizations especially in the container era right where there are certain companies who are actually I would argue even bypassing you know and building it container first strategy as opposed to a cloud first strategy right so that's that's the way they are thinking about this and when you talk about route through that lens storage essentially is an application as opposed to infrastructure so you have to talk as three or you have to talk whatever protocol it is so it just becomes part and parcel of that so the challenge or what vendors or customers are looking at us is how can you make it as seamless as possible so that they can get the acceleration can happen because a year ago I think nine months ago there was a survey that was done where customers said the top two issues with moved to accelerated move the containers were storage or persistent storage and security well I think we have a firm handle on what we need to do to really help our customers at least address the storage part of that discussion what's up what and what's the make of the use cases right now how many customers are deploying this roughly order of magnitude mean let's go into details but I mean you know you know how's the migration okay the early adopters and in mode now is it fast followers is it the rest of the market I think it's still in the early adoption in the truest definition I think you know using the baseball analogy we're at the top of the first inning right and most of the workloads tend to be new workloads right there are some left cloud native but there's some but as far as the use cases it is you know across the board you know no sequel databases see I see Dee Jenkins type of environment so we surprised vertical centric either because storage your Stora just used by everybody yes there is one layer where there's certain I is free apps that tend to be focused on certain verticals but they happen right by our availability or I ah she might need financial services or stretch clusters and all those kinds of things okay cool I love the concept of unstitch but that does leave in the cold a little bit the people that we used to call storage admins right so now multi cloud hybrid cloud a lot of examples and blood demonstration operators operators done does the job of the storage the person you who used to be in charge of storage it seems like that that changes now even with unstitch a lot of automation a lot of fabric a lot of pooling does it itself but you still are on a lot of different clouds and things like that how do you how are you talking to customers about that so you know I think one of the I think the term that people have started to use as generalists right if you look at it you know five years ago or seven years ago you had a silo of systems administrators storage administrators and network administrators now the whole vertical store the silos have been in a way normalized so now you have a pool of people might be the major is in storage but the minor is a networking by the major isn't compute their minor is in storage so it really helped her all the organizations that we talked to now they say look I have a collective pool that can help me where I need to get to so this plays really well what about that audience absolutely it does and the hybrid cloud equation in your thoughts there cuz lastly we'll keep on did a great piece of research on true private cloud and there and they are looking for more folks to first rating the next set of surveys so I'd love to introduce you to the Peter verse over there but the point is was true private cloud report showed that on-premise cloud if occasion whatever you want to call it action was much higher and growing it's not so much on premise has been dying or being reduced its transitioning to on-premise cloud operations which is essentially cloud it's a fat edge or you know and the cloud is that what is the cloud so you're seeing still a lot of work being done on premises where there we recasting reimagining cloud so how is that impacting the hybrid cloud because I've been cause it's not really a product it's a yes it's a journey it's a connection between two clouds so storage data the data plane control planes are all kind of like evolving your thoughts on multi cloud and as hybrid starts to accelerate that's the path I'll see open shift but your thoughts on so the I think the way we think about this right is hybrid cloud it's not so much that everything is running on app I absolutely agree with the research right but the customers that we talk to they are still building the foundational business on I got a you know keep the private cloud make it as seamless and as efficient as possible but there are certain workloads that lend themselves well to running on a public cloud now it's not so much as a disjointed these two universes never talk to each other it's how do we the Red Hat try to bring the two together so the user experience you almost in a way try to minimize where it actually runs right now so that's that's an open shift is a classic example of that right where you're running it on Prem but you're also running on these public clouds if certain workloads that are great on a public cloud an example of that is one of the largest airports and Europe so they use OpenShift on Prem but they also use OpenShift on a couple of other public clouds and our CNS product which is a continuous tourist product run runs along all those three environments so to the end-user it's essentially a seamless experience and that's you know as the journey unfolds I think that's what you're going to see more and more is about how do we start to know that the storage foundation is built how do we start to exposed some data services that can run across all these different that's gonna be killer so here's an update in the business how's the business what's the road map looked like what are the things that you guys are working on what's the priorities so business is like we announced on Monday 150 net new customers over the last 12 months and that's just on one specific strategic imperative which is containing native storage or help the customers with a container journey besides that I think there are two other pillars we are focused on one is around hybrid cloud right which is how do you really provide the best storage substrate for customers building private clouds and hybrid clouds and the third part is hyperconvergence because I think what our customers are asking for is they've seen the power of hyperconvergence but they want an open-source variant of hyperconvergence for their environment and stay tuned on that front we got some exciting stuff going on and we'll keep you folks updated on the final question what's going on the show here for you what's notable but the folks that are watching who couldn't make it here what's the vibe what's the hall way conversation what's the customer conversate with steer some color of what's happening here at breadhead summit here in san francisco so a lot of things but then I wish we had time but I know we're short of time here but the few things I want to highlight one is all the technology demos that we did yesterday today and some in the tomorrow or tomorrow timeframe you'll see are containing native storage or a storage portfolio be an integral part of every one of the teams that we are talking about so that's you know and we've got very positive feedback on that we have over two dozen text sessions and my understanding is I don't go to those my understandings they've all been standing room only so there is definite customer interest in where we are what we're doing so we this show has been awesome so for us yeah storage is the gift that keeps on giving now it's going to be storage less than unstirred whatever word you want I like storage list because it sounds like server list which doesn't mean anything either but it sounds good but it's a full of resource gratulations I play it's a hot area certainly having programmable infrastructure means better development time and certainly making it you know elastic and making it horizontally scalable is the dream we all want to get to fast so be there more live coverage bringing you all the action here in the in the open here at Moscone you're in the mill the floors the cube coverage of Red Hat Summer 2018 I'm John Fourier with John Troy your stay with us we back with more after this short break thanks John

>> Announcer: Live, from Las Vegas, it's theCUBE, covering AWS re:Invent 2017. Presented by AWS, Intel, and our ecosystem of partners. >> Welcome back everyone, live here in Las Vegas it's AWS re:Invent, it's theCUBE exclusive coverage of re:Invent 2017, our 5th year covering Amazon, watching the explosive growth. I'm John Furrier, the co-founder of SiliconANGLE Media, with Justin Warren my co-host here for this segment. Our next guest is Raejeanne Skillern, Vice President of the Data Center Group and also General Manager of the Cloud Service Provider, part of Intel. Welcome to theCUBE. >> Thank you very much, it's nice to be here again. >> So, Intel inside the Cloud is big growth for you, what's the numbers look like, your earnings look pretty good, what's the business update? >> You know, it is growing, and it's growing in so many different angles, it's coming from multiple fronts. Part of it is just these killer workloads that are driving the need for change. Artificial Intelligence, immersive media, network transformation to handle all the data. It is causing this real spur in growth, from both the very largest of Cloud service providers across the ecosystem, and one of the things when I look at the growth, I try to say, now where is it really coming from? Yes, Amazon is just insanely growing fast and the big guys are doing well, their global expansion, But, also, the next wave of Cloud service providers, and regions, and different countries, are doing really well too, they're actually growing faster, so the whole ecosystem is growing with demand. >> Yeah, it's certainly not slowing down. I can't see any way in which that it would slow down at all, if anything it's just gonna get faster. >> I think so, and when I track trends like CapEx spending and analyst predictions on the future, consumer demand, everything indicates we're gonna see another strong 5-10 years. >> Justin: Yep. >> So how do the Cloud service providers shake out because if you look at this creativity of software renaissance, the interactivity, all the amazing use cases, from Thorn, finding missing exploited children, to, you know, high end HPC scientists, genome sequencing stuff. Massive use cases. >> Raejeanne: Right. >> So that's going to be a tsunami of software developers, Changing. How does that shape the Cloud providers, does it segment them into tiers, how are you guys looking at that business? >> Well I think it breeds a lot of new opportunity. So, yes, you see more and more new services and capabilities to enable developers to more easily develop code, to more quickly, to get better utilization out of their code. But I also think just the concept of a mocking performance and ease, means new insights, new services, new capabilities, things that we just couldn't, a developer, we could not have done before. So it's that, just like electricity, when you make it cheaper and easier to consume, we use more of it. I think Cloud computing is the same way. So we're seeing kind of a natural tam expansion, market expansion, around these things you just couldn't do without the Cloud. >> And new workloads, too. >> And new workloads. >> And all of those workloads, Intel is basically completely repositioning itself from just being a chip company, it's like, you're a data company now. So, what are some of the things that Intel's doing to help people understand how they can make use of these new software techniques, and these new tools, and the capabilities of the Intel chips they're dependent on. So what are some of the things that Intel's doing there? >> So obviously it starts at the bottom, with the best silicon, not just compute, but compute, network, and storage, and accelerators, for all different workloads. We move up to the platforms, we do a lot of hardware engineering with the ecosystem, with our top CSPs, actually many top CSPs, we do direct engineering work to get better systems of architecture. We have a host of libraries we're creating that ease of use, and mocking performance out of it. Reference architectures, co-partners, and solutions. So for us when we talk about being a data company, we can't do it from just even being a chip, we have to be a solutions partner, with Cloud service providers, enterprise, IOT edge solutions, we try to be there. >> You guys always enabled some very cool demos in the day, even back on the PC you always had interactivity, you pioneered multimedia. You always had that eye on the applications. Okay, on stage today all this greatness is out there: NFL demos, all this cool stuff. That's really powering your business, so talk about your relationship with Amazon web services, what's it like, how do you guys engage with them, what's the relationship, 'cause you guys are power engine for AWS. >> Yeah, well we try to be. We wanna be the best performance into their data center. We've had a many, many, many year deep relationship with Amazon. It started from simple co-development and engineering, and is extended much more pervasively across their environment and it poured sometimes into the services. We just, we want to one, make sure that what we're delivering today has been already optimized specifically for their unique environment, and that means we have to start a year or two before, if not earlier, to really understand where they're going, and get their feedback, so that when we either optimize a product or technology across compute network or storage, or create something potentially custom for them, right, it takes a number of years of work, so our partnership has a lot of in-depth engineering, it has a lot of future and near-term enabling, and then we hope to see an expansion. What we really want to do is use our technology to differentiate Amazon. We want their services better because of the technologies or capabilities we put in, so wherever they wanna align in terms of strategic investment and growth markets, we wanna make sure the silicon can enable it. >> Intel always marched to the cadence of Moore's law, and you guys have always been a rapid machine, execution. Amazon looks good, I mean, they're executing. >> Raejeanne: They're fast. >> I mean, what's it like? Share some story, I mean, they're years ahead, what's it like working with Amazon? >> Well I think, I mean, they are fast. It's amazing how quickly they can move and innovate, how rapidly those innovations roll out to the market. I will be honest, there are times where we miss windows because we are slow, and they just look at us like, "Well we told you "this four weeks ago, here it is, right?" (laughing) >> Can't design a chip in four weeks. We don't have kubernetes, no containers for chips. >> I mean they are just, they have it down. So what we're trying to do is part of this transition from being a client company, to a Cloud and data center company, and IOT company, means everything we have to do is faster. So we have to design our chips more quickly, we have to put in more modularity for faster derivatives, and we have to move at Cloud speed, not classic Intel speed. >> Right, yeah. So what are some of the lessons that other companies can take from Intel, I mean it's a hardware company, or it was originally a hardware company, and now you've transitioned to being a Cloud company, and you're being pushed by Amazon to move faster and faster. So what are some of the lessons that you can share with other companies who are trying to start moving at Cloud speed? >> You know, I think, I love Jeff Bezos' approach, customer-obsessed. If you aren't understanding how the end customers, starting with Amazon's customers, but also then my customer, Amazon, how they're using and consuming technology, we can't really create good technology. I would say a lot of companies create a great thing and then try to go sell it at markets, >> Yep. >> Versus starting with the market, and creating the specific thing. The other thing we've learned, I mean, Intel is a very data driven company, both in our decision making, as well as our company growth, this is, and we talk about it from a developer envirogroup, but it's the same iterate fast. Fail quickly and move on. You don't need perfect. This is one of our learnings, right? Don't wait six months for perfect, move fast, get 70-80% of the way there, I've heard governments say they get 40-50 way percent of there, make decisions, because they have to move that quickly. For military or other exercises. So what we're trying to do is match that type of speed as well. >> It's a world of compute now, I mean, I was at Alibaba Conference, they had their Cloud coachings hearing it here, the same message: more compute. They're not saying I need more little chips, they're saying I want more horsepower. >> Raejeanne: Right. >> And you guys just announced the C5 instance, recently, a couple weeks. >> And the budget. >> What is that gonna do? I mean, it's fairly new. >> Raejeanne: Yes. >> What does that mean? Is that gonna be an IOT edge opportunity? Is it all workloads? That's gonna be like, a pretty big deal. What's your take on it? >> It is, their C instant line has always been for high performances growing workloads. We're seeing like, for HPC workloads, anywhere from two to four-and-a-half X the performance moving from C4 to C5, right? This is an instance that can handle the most demanding workloads from high performance compute to artificial Intelligence to others. So, you know, we have our latest and greatest Intel ZN scalable process in there, a very high performant one, that we customized specifically for their environment, but then they do all this amazing software work and efficiency work around it, to really unlock. I was really glad to see when they talked about those C5 instances launching, 25% on workload's price performance up to 50% price performance improvements on some others. So, I mean, once again, when you can take more compute and make it more cost effective, it's just a lot more things people can do in the industry, so we're very excited about that instance. >> What's the biggest thing in the past five years that jumped out at you in the massive change in the industry? Application, startups, business growth? What amazes you? >> Yeah, there's so much, I would kind of combine it under what the industry calls Digital Transformation. You know, when I look at it, one of the points that always sticks in my mind, is CEOs, 70% of them have digital transformation at the heart because the data suggests that by the end of 2018, the top 40% of the top 20, 40% of the top 20 industries are gonna be disrupted. So that to me, that amount of disruption happening, and the company's trying to disrupt themselves whether it's healthcare, retail, manufacturing, oil and glass, the use and pervasiveness of where technology and Cloud can fit has really kind of astonished me and I love, once again, I love that they're making what they do today better. But the new things that they're doing, I mean, in healthcare, right? It's just amazing. >> I mean, we used to use the word back in the day when I broke into the biz in the '80s, Data Processing Department. I mean, the Cloud is one big data processor. >> It is. It is a compute power. We call it the brain, right? If Cloud is now ubiquitous, right? It is, from public Cloud to private to Edge, and everything in between, it's that brain, right? >> John: It's the brain. >> That's enabling the compute, so we have to-- >> You people have always been on the inside of everything, so congratulations on your success, on the Cloud growth. General manager of the Cloud service provider and Vice President of Data Center Group, Raejeanne Skillern, here inside theCUBE with Intel, I'm Jeff Furrier with Justin Warren, back with more live coverage, here in Las Vegas, for Amazon web services 2017 re:Invent. More after this short break. (techno music)

>> There's a movement that's sweeping across businesses everywhere here in this country and around the world. And it's all about data. Today businesses are being inundated with data. To the tune of over two and a half million gigabytes that'll be generated in the next 60 seconds alone. What do you do with all that data? To extract insights you typically turn to a data scientist. But not necessarily anymore. At least not exclusively. Today the ability to extract value from data is becoming a shared mission. A team effort that spans the organization extending far more widely than ever before. Today, data science is being democratized. >> Data Sciences for All: It's a Whole New Game. >> Welcome everyone, I'm Katie Linendoll. I'm a technology expert writer and I love reporting on all things tech. My fascination with tech started very young. I began coding when I was 12. Received my networking certs by 18 and a degree in IT and new media from Rochester Institute of Technology. So as you can tell, technology has always been a sure passion of mine. Having grown up in the digital age, I love having a career that keeps me at the forefront of science and technology innovations. I spend equal time in the field being hands on as I do on my laptop conducting in depth research. Whether I'm diving underwater with NASA astronauts, witnessing the new ways which mobile technology can help rebuild the Philippine's economy in the wake of super typhoons, or sharing a first look at the newest iPhones on The Today Show, yesterday, I'm always on the hunt for the latest and greatest tech stories. And that's what brought me here. I'll be your host for the next hour and as we explore the new phenomenon that is taking businesses around the world by storm. And data science continues to become democratized and extends beyond the domain of the data scientist. And why there's also a mandate for all of us to become data literate. Now that data science for all drives our AI culture. And we're going to be able to take to the streets and go behind the scenes as we uncover the factors that are fueling this phenomenon and giving rise to a movement that is reshaping how businesses leverage data. And putting organizations on the road to AI. So coming up, I'll be doing interviews with data scientists. We'll see real world demos and take a look at how IBM is changing the game with an open data science platform. We'll also be joined by legendary statistician Nate Silver, founder and editor-in-chief of FiveThirtyEight. Who will shed light on how a data driven mindset is changing everything from business to our culture. We also have a few people who are joining us in our studio, so thank you guys for joining us. Come on, I can do better than that, right? Live studio audience, the fun stuff. And for all of you during the program, I want to remind you to join that conversation on social media using the hashtag DSforAll, it's data science for all. Share your thoughts on what data science and AI means to you and your business. And, let's dive into a whole new game of data science. Now I'd like to welcome my co-host General Manager IBM Analytics, Rob Thomas. >> Hello, Katie. >> Come on guys. >> Yeah, seriously. >> No one's allowed to be quiet during this show, okay? >> Right. >> Or, I'll start calling people out. So Rob, thank you so much. I think you know this conversation, we're calling it a data explosion happening right now. And it's nothing new. And when you and I chatted about it. You've been talking about this for years. You have to ask, is this old news at this point? >> Yeah, I mean, well first of all, the data explosion is not coming, it's here. And everybody's in the middle of it right now. What is different is the economics have changed. And the scale and complexity of the data that organizations are having to deal with has changed. And to this day, 80% of the data in the world still sits behind corporate firewalls. So, that's becoming a problem. It's becoming unmanageable. IT struggles to manage it. The business can't get everything they need. Consumers can't consume it when they want. So we have a challenge here. >> It's challenging in the world of unmanageable. Crazy complexity. If I'm sitting here as an IT manager of my business, I'm probably thinking to myself, this is incredibly frustrating. How in the world am I going to get control of all this data? And probably not just me thinking it. Many individuals here as well. >> Yeah, indeed. Everybody's thinking about how am I going to put data to work in my organization in a way I haven't done before. Look, you've got to have the right expertise, the right tools. The other thing that's happening in the market right now is clients are dealing with multi cloud environments. So data behind the firewall in private cloud, multiple public clouds. And they have to find a way. How am I going to pull meaning out of this data? And that brings us to data science and AI. That's how you get there. >> I understand the data science part but I think we're all starting to hear more about AI. And it's incredible that this buzz word is happening. How do businesses adopt to this AI growth and boom and trend that's happening in this world right now? >> Well, let me define it this way. Data science is a discipline. And machine learning is one technique. And then AI puts both machine learning into practice and applies it to the business. So this is really about how getting your business where it needs to go. And to get to an AI future, you have to lay a data foundation today. I love the phrase, "there's no AI without IA." That means you're not going to get to AI unless you have the right information architecture to start with. >> Can you elaborate though in terms of how businesses can really adopt AI and get started. >> Look, I think there's four things you have to do if you're serious about AI. One is you need a strategy for data acquisition. Two is you need a modern data architecture. Three is you need pervasive automation. And four is you got to expand job roles in the organization. >> Data acquisition. First pillar in this you just discussed. Can we start there and explain why it's so critical in this process? >> Yeah, so let's think about how data acquisition has evolved through the years. 15 years ago, data acquisition was about how do I get data in and out of my ERP system? And that was pretty much solved. Then the mobile revolution happens. And suddenly you've got structured and non-structured data. More than you've ever dealt with. And now you get to where we are today. You're talking terabytes, petabytes of data. >> [Katie] Yottabytes, I heard that word the other day. >> I heard that too. >> Didn't even know what it meant. >> You know how many zeros that is? >> I thought we were in Star Wars. >> Yeah, I think it's a lot of zeroes. >> Yodabytes, it's new. >> So, it's becoming more and more complex in terms of how you acquire data. So that's the new data landscape that every client is dealing with. And if you don't have a strategy for how you acquire that and manage it, you're not going to get to that AI future. >> So a natural segue, if you are one of these businesses, how do you build for the data landscape? >> Yeah, so the question I always hear from customers is we need to evolve our data architecture to be ready for AI. And the way I think about that is it's really about moving from static data repositories to more of a fluid data layer. >> And we continue with the architecture. New data architecture is an interesting buzz word to hear. But it's also one of the four pillars. So if you could dive in there. >> Yeah, I mean it's a new twist on what I would call some core data science concepts. For example, you have to leverage tools with a modern, centralized data warehouse. But your data warehouse can't be stagnant to just what's right there. So you need a way to federate data across different environments. You need to be able to bring your analytics to the data because it's most efficient that way. And ultimately, it's about building an optimized data platform that is designed for data science and AI. Which means it has to be a lot more flexible than what clients have had in the past. >> All right. So we've laid out what you need for driving automation. But where does the machine learning kick in? >> Machine learning is what gives you the ability to automate tasks. And I think about machine learning. It's about predicting and automating. And this will really change the roles of data professionals and IT professionals. For example, a data scientist cannot possibly know every algorithm or every model that they could use. So we can automate the process of algorithm selection. Another example is things like automated data matching. Or metadata creation. Some of these things may not be exciting but they're hugely practical. And so when you think about the real use cases that are driving return on investment today, it's things like that. It's automating the mundane tasks. >> Let's go ahead and come back to something that you mentioned earlier because it's fascinating to be talking about this AI journey, but also significant is the new job roles. And what are those other participants in the analytics pipeline? >> Yeah I think we're just at the start of this idea of new job roles. We have data scientists. We have data engineers. Now you see machine learning engineers. Application developers. What's really happening is that data scientists are no longer allowed to work in their own silo. And so the new job roles is about how does everybody have data first in their mind? And then they're using tools to automate data science, to automate building machine learning into applications. So roles are going to change dramatically in organizations. >> I think that's confusing though because we have several organizations who saying is that highly specialized roles, just for data science? Or is it applicable to everybody across the board? >> Yeah, and that's the big question, right? Cause everybody's thinking how will this apply? Do I want this to be just a small set of people in the organization that will do this? But, our view is data science has to for everybody. It's about bring data science to everybody as a shared mission across the organization. Everybody in the company has to be data literate. And participate in this journey. >> So overall, group effort, has to be a common goal, and we all need to be data literate across the board. >> Absolutely. >> Done deal. But at the end of the day, it's kind of not an easy task. >> It's not. It's not easy but it's maybe not as big of a shift as you would think. Because you have to put data in the hands of people that can do something with it. So, it's very basic. Give access to data. Data's often locked up in a lot of organizations today. Give people the right tools. Embrace the idea of choice or diversity in terms of those tools. That gets you started on this path. >> It's interesting to hear you say essentially you need to train everyone though across the board when it comes to data literacy. And I think people that are coming into the work force don't necessarily have a background or a degree in data science. So how do you manage? >> Yeah, so in many cases that's true. I will tell you some universities are doing amazing work here. One example, University of California Berkeley. They offer a course for all majors. So no matter what you're majoring in, you have a course on foundations of data science. How do you bring data science to every role? So it's starting to happen. We at IBM provide data science courses through CognitiveClass.ai. It's for everybody. It's free. And look, if you want to get your hands on code and just dive right in, you go to datascience.ibm.com. The key point is this though. It's more about attitude than it is aptitude. I think anybody can figure this out. But it's about the attitude to say we're putting data first and we're going to figure out how to make this real in our organization. >> I also have to give a shout out to my alma mater because I have heard that there is an offering in MS in data analytics. And they are always on the forefront of new technologies and new majors and on trend. And I've heard that the placement behind those jobs, people graduating with the MS is high. >> I'm sure it's very high. >> So go Tigers. All right, tangential. Let me get back to something else you touched on earlier because you mentioned that a number of customers ask you how in the world do I get started with AI? It's an overwhelming question. Where do you even begin? What do you tell them? >> Yeah, well things are moving really fast. But the good thing is most organizations I see, they're already on the path, even if they don't know it. They might have a BI practice in place. They've got data warehouses. They've got data lakes. Let me give you an example. AMC Networks. They produce a lot of the shows that I'm sure you watch Katie. >> [Katie] Yes, Breaking Bad, Walking Dead, any fans? >> [Rob] Yeah, we've got a few. >> [Katie] Well you taught me something I didn't even know. Because it's amazing how we have all these different industries, but yet media in itself is impacted too. And this is a good example. >> Absolutely. So, AMC Networks, think about it. They've got ads to place. They want to track viewer behavior. What do people like? What do they dislike? So they have to optimize every aspect of their business from marketing campaigns to promotions to scheduling to ads. And their goal was transform data into business insights and really take the burden off of their IT team that was heavily burdened by obviously a huge increase in data. So their VP of BI took the approach of using machine learning to process large volumes of data. They used a platform that was designed for AI and data processing. It's the IBM analytics system where it's a data warehouse, data science tools are built in. It has in memory data processing. And just like that, they were ready for AI. And they're already seeing that impact in their business. >> Do you think a movement of that nature kind of presses other media conglomerates and organizations to say we need to be doing this too? >> I think it's inevitable that everybody, you're either going to be playing, you're either going to be leading, or you'll be playing catch up. And so, as we talk to clients we think about how do you start down this path now, even if you have to iterate over time? Because otherwise you're going to wake up and you're going to be behind. >> One thing worth noting is we've talked about analytics to the data. It's analytics first to the data, not the other way around. >> Right. So, look. We as a practice, we say you want to bring data to where the data sits. Because it's a lot more efficient that way. It gets you better outcomes in terms of how you train models and it's more efficient. And we think that leads to better outcomes. Other organization will say, "Hey move the data around." And everything becomes a big data movement exercise. But once an organization has started down this path, they're starting to get predictions, they want to do it where it's really easy. And that means analytics applied right where the data sits. >> And worth talking about the role of the data scientist in all of this. It's been called the hot job of the decade. And a Harvard Business Review even dubbed it the sexiest job of the 21st century. >> Yes. >> I want to see this on the cover of Vogue. Like I want to see the first data scientist. Female preferred, on the cover of Vogue. That would be amazing. >> Perhaps you can. >> People agree. So what changes for them? Is this challenging in terms of we talk data science for all. Where do all the data science, is it data science for everyone? And how does it change everything? >> Well, I think of it this way. AI gives software super powers. It really does. It changes the nature of software. And at the center of that is data scientists. So, a data scientist has a set of powers that they've never had before in any organization. And that's why it's a hot profession. Now, on one hand, this has been around for a while. We've had actuaries. We've had statisticians that have really transformed industries. But there are a few things that are new now. We have new tools. New languages. Broader recognition of this need. And while it's important to recognize this critical skill set, you can't just limit it to a few people. This is about scaling it across the organization. And truly making it accessible to all. >> So then do we need more data scientists? Or is this something you train like you said, across the board? >> Well, I think you want to do a little bit of both. We want more. But, we can also train more and make the ones we have more productive. The way I think about it is there's kind of two markets here. And we call it clickers and coders. >> [Katie] I like that. That's good. >> So, let's talk about what that means. So clickers are basically somebody that wants to use tools. Create models visually. It's drag and drop. Something that's very intuitive. Those are the clickers. Nothing wrong with that. It's been valuable for years. There's a new crop of data scientists. They want to code. They want to build with the latest open source tools. They want to write in Python or R. These are the coders. And both approaches are viable. Both approaches are critical. Organizations have to have a way to meet the needs of both of those types. And there's not a lot of things available today that do that. >> Well let's keep going on that. Because I hear you talking about the data scientists role and how it's critical to success, but with the new tools, data science and analytics skills can extend beyond the domain of just the data scientist. >> That's right. So look, we're unifying coders and clickers into a single platform, which we call IBM Data Science Experience. And as the demand for data science expertise grows, so does the need for these kind of tools. To bring them into the same environment. And my view is if you have the right platform, it enables the organization to collaborate. And suddenly you've changed the nature of data science from an individual sport to a team sport. >> So as somebody that, my background is in IT, the question is really is this an additional piece of what IT needs to do in 2017 and beyond? Or is it just another line item to the budget? >> So I'm afraid that some people might view it that way. As just another line item. But, I would challenge that and say data science is going to reinvent IT. It's going to change the nature of IT. And every organization needs to think about what are the skills that are critical? How do we engage a broader team to do this? Because once they get there, this is the chance to reinvent how they're performing IT. >> [Katie] Challenging or not? >> Look it's all a big challenge. Think about everything IT organizations have been through. Some of them were late to things like mobile, but then they caught up. Some were late to cloud, but then they caught up. I would just urge people, don't be late to data science. Use this as your chance to reinvent IT. Start with this notion of clickers and coders. This is a seminal moment. Much like mobile and cloud was. So don't be late. >> And I think it's critical because it could be so costly to wait. And Rob and I were even chatting earlier how data analytics is just moving into all different kinds of industries. And I can tell you even personally being effected by how important the analysis is in working in pediatric cancer for the last seven years. I personally implement virtual reality headsets to pediatric cancer hospitals across the country. And it's great. And it's working phenomenally. And the kids are amazed. And the staff is amazed. But the phase two of this project is putting in little metrics in the hardware that gather the breathing, the heart rate to show that we have data. Proof that we can hand over to the hospitals to continue making this program a success. So just in-- >> That's a great example. >> An interesting example. >> Saving lives? >> Yes. >> That's also applying a lot of what we talked about. >> Exciting stuff in the world of data science. >> Yes. Look, I just add this is an existential moment for every organization. Because what you do in this area is probably going to define how competitive you are going forward. And think about if you don't do something. What if one of your competitors goes and creates an application that's more engaging with clients? So my recommendation is start small. Experiment. Learn. Iterate on projects. Define the business outcomes. Then scale up. It's very doable. But you've got to take the first step. >> First step always critical. And now we're going to get to the fun hands on part of our story. Because in just a moment we're going to take a closer look at what data science can deliver. And where organizations are trying to get to. All right. Thank you Rob and now we've been joined by Siva Anne who is going to help us navigate this demo. First, welcome Siva. Give him a big round of applause. Yeah. All right, Rob break down what we're going to be looking at. You take over this demo. >> All right. So this is going to be pretty interesting. So Siva is going to take us through. So he's going to play the role of a financial adviser. Who wants to help better serve clients through recommendations. And I'm going to really illustrate three things. One is how do you federate data from multiple data sources? Inside the firewall, outside the firewall. How do you apply machine learning to predict and to automate? And then how do you move analytics closer to your data? So, what you're seeing here is a custom application for an investment firm. So, Siva, our financial adviser, welcome. So you can see at the top, we've got market data. We pulled that from an external source. And then we've got Siva's calendar in the middle. He's got clients on the right side. So page down, what else do you see down there Siva? >> [Siva] I can see the recent market news. And in here I can see that JP Morgan is calling for a US dollar rebound in the second half of the year. And, I have upcoming meeting with Leo Rakes. I can get-- >> [Rob] So let's go in there. Why don't you click on Leo Rakes. So, you're sitting at your desk, you're deciding how you're going to spend the day. You know you have a meeting with Leo. So you click on it. You immediately see, all right, so what do we know about him? We've got data governance implemented. So we know his age, we know his degree. We can see he's not that aggressive of a trader. Only six trades in the last few years. But then where it gets interesting is you go to the bottom. You start to see predicted industry affinity. Where did that come from? How do we have that? >> [Siva] So these green lines and red arrows here indicate the trending affinity of Leo Rakes for particular industry stocks. What we've done here is we've built machine learning models using customer's demographic data, his stock portfolios, and browsing behavior to build a model which can predict his affinity for a particular industry. >> [Rob] Interesting. So, I like to think of this, we call it celebrity experiences. So how do you treat every customer like they're a celebrity? So to some extent, we're reading his mind. Because without asking him, we know that he's going to have an affinity for auto stocks. So we go down. Now we look at his portfolio. You can see okay, he's got some different holdings. He's got Amazon, Google, Apple, and then he's got RACE, which is the ticker for Ferrari. You can see that's done incredibly well. And so, as a financial adviser, you look at this and you say, all right, we know he loves auto stocks. Ferrari's done very well. Let's create a hedge. Like what kind of security would interest him as a hedge against his position for Ferrari? Could we go figure that out? >> [Siva] Yes. Given I know that he's gotten an affinity for auto stocks, and I also see that Ferrari has got some terminus gains, I want to lock in these gains by hedging. And I want to do that by picking a auto stock which has got negative correlation with Ferrari. >> [Rob] So this is where we get to the idea of in database analytics. Cause you start clicking that and immediately we're getting instant answers of what's happening. So what did we find here? We're going to compare Ferrari and Honda. >> [Siva] I'm going to compare Ferrari with Honda. And what I see here instantly is that Honda has got a negative correlation with Ferrari, which makes it a perfect mix for his stock portfolio. Given he has an affinity for auto stocks and it correlates negatively with Ferrari. >> [Rob] These are very powerful tools at the hand of a financial adviser. You think about it. As a financial adviser, you wouldn't think about federating data, machine learning, pretty powerful. >> [Siva] Yes. So what we have seen here is that using the common SQL engine, we've been able to federate queries across multiple data sources. Db2 Warehouse in the cloud, IBM's Integrated Analytic System, and Hortonworks powered Hadoop platform for the new speeds. We've been able to use machine learning to derive innovative insights about his stock affinities. And drive the machine learning into the appliance. Closer to where the data resides to deliver high performance analytics. >> [Rob] At scale? >> [Siva] We're able to run millions of these correlations across stocks, currency, other factors. And even score hundreds of customers for their affinities on a daily basis. >> That's great. Siva, thank you for playing the role of financial adviser. So I just want to recap briefly. Cause this really powerful technology that's really simple. So we federated, we aggregated multiple data sources from all over the web and internal systems. And public cloud systems. Machine learning models were built that predicted Leo's affinity for a certain industry. In this case, automotive. And then you see when you deploy analytics next to your data, even a financial adviser, just with the click of a button is getting instant answers so they can go be more productive in their next meeting. This whole idea of celebrity experiences for your customer, that's available for everybody, if you take advantage of these types of capabilities. Katie, I'll hand it back to you. >> Good stuff. Thank you Rob. Thank you Siva. Powerful demonstration on what we've been talking about all afternoon. And thank you again to Siva for helping us navigate. Should be give him one more round of applause? We're going to be back in just a moment to look at how we operationalize all of this data. But in first, here's a message from me. If you're a part of a line of business, your main fear is disruption. You know data is the new goal that can create huge amounts of value. So does your competition. And they may be beating you to it. You're convinced there are new business models and revenue sources hidden in all the data. You just need to figure out how to leverage it. But with the scarcity of data scientists, you really can't rely solely on them. You may need more people throughout the organization that have the ability to extract value from data. And as a data science leader or data scientist, you have a lot of the same concerns. You spend way too much time looking for, prepping, and interpreting data and waiting for models to train. You know you need to operationalize the work you do to provide business value faster. What you want is an easier way to do data prep. And rapidly build models that can be easily deployed, monitored and automatically updated. So whether you're a data scientist, data science leader, or in a line of business, what's the solution? What'll it take to transform the way you work? That's what we're going to explore next. All right, now it's time to delve deeper into the nuts and bolts. The nitty gritty of operationalizing data science and creating a data driven culture. How do you actually do that? Well that's what these experts are here to share with us. I'm joined by Nir Kaldero, who's head of data science at Galvanize, which is an education and training organization. Tricia Wang, who is co-founder of Sudden Compass, a consultancy that helps companies understand people with data. And last, but certainly not least, Michael Li, founder and CEO of Data Incubator, which is a data science train company. All right guys. Shall we get right to it? >> All right. >> So data explosion happening right now. And we are seeing it across the board. I just shared an example of how it's impacting my philanthropic work in pediatric cancer. But you guys each have so many unique roles in your business life. How are you seeing it just blow up in your fields? Nir, your thing? >> Yeah, for example like in Galvanize we train many Fortune 500 companies. And just by looking at the demand of companies that wants us to help them go through this digital transformation is mind-blowing. Data point by itself. >> Okay. Well what we're seeing what's going on is that data science like as a theme, is that it's actually for everyone now. But what's happening is that it's actually meeting non technical people. But what we're seeing is that when non technical people are implementing these tools or coming at these tools without a base line of data literacy, they're often times using it in ways that distance themselves from the customer. Because they're implementing data science tools without a clear purpose, without a clear problem. And so what we do at Sudden Compass is that we work with companies to help them embrace and understand the complexity of their customers. Because often times they are misusing data science to try and flatten their understanding of the customer. As if you can just do more traditional marketing. Where you're putting people into boxes. And I think the whole ROI of data is that you can now understand people's relationships at a much more complex level at a greater scale before. But we have to do this with basic data literacy. And this has to involve technical and non technical people. >> Well you can have all the data in the world, and I think it speaks to, if you're not doing the proper movement with it, forget it. It means nothing at the same time. >> No absolutely. I mean, I think that when you look at the huge explosion in data, that comes with it a huge explosion in data experts. Right, we call them data scientists, data analysts. And sometimes they're people who are very, very talented, like the people here. But sometimes you have people who are maybe re-branding themselves, right? Trying to move up their title one notch to try to attract that higher salary. And I think that that's one of the things that customers are coming to us for, right? They're saying, hey look, there are a lot of people that call themselves data scientists, but we can't really distinguish. So, we have sort of run a fellowship where you help companies hire from a really talented group of folks, who are also truly data scientists and who know all those kind of really important data science tools. And we also help companies internally. Fortune 500 companies who are looking to grow that data science practice that they have. And we help clients like McKinsey, BCG, Bain, train up their customers, also their clients, also their workers to be more data talented. And to build up that data science capabilities. >> And Nir, this is something you work with a lot. A lot of Fortune 500 companies. And when we were speaking earlier, you were saying many of these companies can be in a panic. >> Yeah. >> Explain that. >> Yeah, so you know, not all Fortune 500 companies are fully data driven. And we know that the winners in this fourth industrial revolution, which I like to call the machine intelligence revolution, will be companies who navigate and transform their organization to unlock the power of data science and machine learning. And the companies that are not like that. Or not utilize data science and predictive power well, will pretty much get shredded. So they are in a panic. >> Tricia, companies have to deal with data behind the firewall and in the new multi cloud world. How do organizations start to become driven right to the core? >> I think the most urgent question to become data driven that companies should be asking is how do I bring the complex reality that our customers are experiencing on the ground in to a corporate office? Into the data models. So that question is critical because that's how you actually prevent any big data disasters. And that's how you leverage big data. Because when your data models are really far from your human models, that's when you're going to do things that are really far off from how, it's going to not feel right. That's when Tesco had their terrible big data disaster that they're still recovering from. And so that's why I think it's really important to understand that when you implement big data, you have to further embrace thick data. The qualitative, the emotional stuff, that is difficult to quantify. But then comes the difficult art and science that I think is the next level of data science. Which is that getting non technical and technical people together to ask how do we find those unknown nuggets of insights that are difficult to quantify? Then, how do we do the next step of figuring out how do you mathematically scale those insights into a data model? So that actually is reflective of human understanding? And then we can start making decisions at scale. But you have to have that first. >> That's absolutely right. And I think that when we think about what it means to be a data scientist, right? I always think about it in these sort of three pillars. You have the math side. You have to have that kind of stats, hardcore machine learning background. You have the programming side. You don't work with small amounts of data. You work with large amounts of data. You've got to be able to type the code to make those computers run. But then the last part is that human element. You have to understand the domain expertise. You have to understand what it is that I'm actually analyzing. What's the business proposition? And how are the clients, how are the users actually interacting with the system? That human element that you were talking about. And I think having somebody who understands all of those and not just in isolation, but is able to marry that understanding across those different topics, that's what makes a data scientist. >> But I find that we don't have people with those skill sets. And right now the way I see teams being set up inside companies is that they're creating these isolated data unicorns. These data scientists that have graduated from your programs, which are great. But, they don't involve the people who are the domain experts. They don't involve the designers, the consumer insight people, the people, the salespeople. The people who spend time with the customers day in and day out. Somehow they're left out of the room. They're consulted, but they're not a stakeholder. >> Can I actually >> Yeah, yeah please. >> Can I actually give a quick example? So for example, we at Galvanize train the executives and the managers. And then the technical people, the data scientists and the analysts. But in order to actually see all of the RY behind the data, you also have to have a creative fluid conversation between non technical and technical people. And this is a major trend now. And there's a major gap. And we need to increase awareness and kind of like create a new, kind of like environment where technical people also talks seamlessly with non technical ones. >> [Tricia] We call-- >> That's one of the things that we see a lot. Is one of the trends in-- >> A major trend. >> data science training is it's not just for the data science technical experts. It's not just for one type of person. So a lot of the training we do is sort of data engineers. People who are more on the software engineering side learning more about the stats of math. And then people who are sort of traditionally on the stat side learning more about the engineering. And then managers and people who are data analysts learning about both. >> Michael, I think you said something that was of interest too because I think we can look at IBM Watson as an example. And working in healthcare. The human component. Because often times we talk about machine learning and AI, and data and you get worried that you still need that human component. Especially in the world of healthcare. And I think that's a very strong point when it comes to the data analysis side. Is there any particular example you can speak to of that? >> So I think that there was this really excellent paper a while ago talking about all the neuro net stuff and trained on textual data. So looking at sort of different corpuses. And they found that these models were highly, highly sexist. They would read these corpuses and it's not because neuro nets themselves are sexist. It's because they're reading the things that we write. And it turns out that we write kind of sexist things. And they would sort of find all these patterns in there that were sort of latent, that had a lot of sort of things that maybe we would cringe at if we sort of saw. And I think that's one of the really important aspects of the human element, right? It's being able to come in and sort of say like, okay, I know what the biases of the system are, I know what the biases of the tools are. I need to figure out how to use that to make the tools, make the world a better place. And like another area where this comes up all the time is lending, right? So the federal government has said, and we have a lot of clients in the financial services space, so they're constantly under these kind of rules that they can't make discriminatory lending practices based on a whole set of protected categories. Race, sex, gender, things like that. But, it's very easy when you train a model on credit scores to pick that up. And then to have a model that's inadvertently sexist or racist. And that's where you need the human element to come back in and say okay, look, you're using the classic example would be zip code, you're using zip code as a variable. But when you look at it, zip codes actually highly correlated with race. And you can't do that. So you may inadvertently by sort of following the math and being a little naive about the problem, inadvertently introduce something really horrible into a model and that's where you need a human element to sort of step in and say, okay hold on. Slow things down. This isn't the right way to go. >> And the people who have -- >> I feel like, I can feel her ready to respond. >> Yes, I'm ready. >> She's like let me have at it. >> And the people here it is. And the people who are really great at providing that human intelligence are social scientists. We are trained to look for bias and to understand bias in data. Whether it's quantitative or qualitative. And I really think that we're going to have less of these kind of problems if we had more integrated teams. If it was a mandate from leadership to say no data science team should be without a social scientist, ethnographer, or qualitative researcher of some kind, to be able to help see these biases. >> The talent piece is actually the most crucial-- >> Yeah. >> one here. If you look about how to enable machine intelligence in organization there are the pillars that I have in my head which is the culture, the talent and the technology infrastructure. And I believe and I saw in working very closely with the Fortune 100 and 200 companies that the talent piece is actually the most important crucial hard to get. >> [Tricia] I totally agree. >> It's absolutely true. Yeah, no I mean I think that's sort of like how we came up with our business model. Companies were basically saying hey, I can't hire data scientists. And so we have a fellowship where we get 2,000 applicants each quarter. We take the top 2% and then we sort of train them up. And we work with hiring companies who then want to hire from that population. And so we're sort of helping them solve that problem. And the other half of it is really around training. Cause with a lot of industries, especially if you're sort of in a more regulated industry, there's a lot of nuances to what you're doing. And the fastest way to develop that data science or AI talent may not necessarily be to hire folks who are coming out of a PhD program. It may be to take folks internally who have a lot of that domain knowledge that you have and get them trained up on those data science techniques. So we've had large insurance companies come to us and say hey look, we hire three or four folks from you a quarter. That doesn't move the needle for us. What we really need is take the thousand actuaries and statisticians that we have and get all of them trained up to become a data scientist and become data literate in this new open source world. >> [Katie] Go ahead. >> All right, ladies first. >> Go ahead. >> Are you sure? >> No please, fight first. >> Go ahead. >> Go ahead Nir. >> So this is actually a trend that we have been seeing in the past year or so that companies kind of like start to look how to upscale and look for talent within the organization. So they can actually move them to become more literate and navigate 'em from analyst to data scientist. And from data scientist to machine learner. So this is actually a trend that is happening already for a year or so. >> Yeah, but I also find that after they've gone through that training in getting people skilled up in data science, the next problem that I get is executives coming to say we've invested in all of this. We're still not moving the needle. We've already invested in the right tools. We've gotten the right skills. We have enough scale of people who have these skills. Why are we not moving the needle? And what I explain to them is look, you're still making decisions in the same way. And you're still not involving enough of the non technical people. Especially from marketing, which is now, the CMO's are much more responsible for driving growth in their companies now. But often times it's so hard to change the old way of marketing, which is still like very segmentation. You know, demographic variable based, and we're trying to move people to say no, you have to understand the complexity of customers and not put them in boxes. >> And I think underlying a lot of this discussion is this question of culture, right? >> Yes. >> Absolutely. >> How do you build a data driven culture? And I think that that culture question, one of the ways that comes up quite often in especially in large, Fortune 500 enterprises, is that they are very, they're not very comfortable with sort of example, open source architecture. Open source tools. And there is some sort of residual bias that that's somehow dangerous. So security vulnerability. And I think that that's part of the cultural challenge that they often have in terms of how do I build a more data driven organization? Well a lot of the talent really wants to use these kind of tools. And I mean, just to give you an example, we are partnering with one of the major cloud providers to sort of help make open source tools more user friendly on their platform. So trying to help them attract the best technologists to use their platform because they want and they understand the value of having that kind of open source technology work seamlessly on their platforms. So I think that just sort of goes to show you how important open source is in this movement. And how much large companies and Fortune 500 companies and a lot of the ones we work with have to embrace that. >> Yeah, and I'm seeing it in our work. Even when we're working with Fortune 500 companies, is that they've already gone through the first phase of data science work. Where I explain it was all about the tools and getting the right tools and architecture in place. And then companies started moving into getting the right skill set in place. Getting the right talent. And what you're talking about with culture is really where I think we're talking about the third phase of data science, which is looking at communication of these technical frameworks so that we can get non technical people really comfortable in the same room with data scientists. That is going to be the phase, that's really where I see the pain point. And that's why at Sudden Compass, we're really dedicated to working with each other to figure out how do we solve this problem now? >> And I think that communication between the technical stakeholders and management and leadership. That's a very critical piece of this. You can't have a successful data science organization without that. >> Absolutely. >> And I think that actually some of the most popular trainings we've had recently are from managers and executives who are looking to say, how do I become more data savvy? How do I figure out what is this data science thing and how do I communicate with my data scientists? >> You guys made this way too easy. I was just going to get some popcorn and watch it play out. >> Nir, last 30 seconds. I want to leave you with an opportunity to, anything you want to add to this conversation? >> I think one thing to conclude is to say that companies that are not data driven is about time to hit refresh and figure how they transition the organization to become data driven. To become agile and nimble so they can actually see what opportunities from this important industrial revolution. Otherwise, unfortunately they will have hard time to survive. >> [Katie] All agreed? >> [Tricia] Absolutely, you're right. >> Michael, Trish, Nir, thank you so much. Fascinating discussion. And thank you guys again for joining us. We will be right back with another great demo. Right after this. >> Thank you Katie. >> Once again, thank you for an excellent discussion. Weren't they great guys? And thank you for everyone who's tuning in on the live webcast. As you can hear, we have an amazing studio audience here. And we're going to keep things moving. I'm now joined by Daniel Hernandez and Siva Anne. And we're going to turn our attention to how you can deliver on what they're talking about using data science experience to do data science faster. >> Thank you Katie. Siva and I are going to spend the next 10 minutes showing you how you can deliver on what they were saying using the IBM Data Science Experience to do data science faster. We'll demonstrate through new features we introduced this week how teams can work together more effectively across the entire analytics life cycle. How you can take advantage of any and all data no matter where it is and what it is. How you could use your favorite tools from open source. And finally how you could build models anywhere and employ them close to where your data is. Remember the financial adviser app Rob showed you? To build an app like that, we needed a team of data scientists, developers, data engineers, and IT staff to collaborate. We do this in the Data Science Experience through a concept we call projects. When I create a new project, I can now use the new Github integration feature. We're doing for data science what we've been doing for developers for years. Distributed teams can work together on analytics projects. And take advantage of Github's version management and change management features. This is a huge deal. Let's explore the project we created for the financial adviser app. As you can see, our data engineer Joane, our developer Rob, and others are collaborating this project. Joane got things started by bringing together the trusted data sources we need to build the app. Taking a closer look at the data, we see that our customer and profile data is stored on our recently announced IBM Integrated Analytics System, which runs safely behind our firewall. We also needed macro economic data, which she was able to find in the Federal Reserve. And she stored it in our Db2 Warehouse on Cloud. And finally, she selected stock news data from NASDAQ.com and landed that in a Hadoop cluster, which happens to be powered by Hortonworks. We added a new feature to the Data Science Experience so that when it's installed with Hortonworks, it automatically uses a need of security and governance controls within the cluster so your data is always secure and safe. Now we want to show you the news data we stored in the Hortonworks cluster. This is the mean administrative console. It's powered by an open source project called Ambari. And here's the news data. It's in parquet files stored in HDFS, which happens to be a distributive file system. To get the data from NASDAQ into our cluster, we used IBM's BigIntegrate and BigQuality to create automatic data pipelines that acquire, cleanse, and ingest that news data. Once the data's available, we use IBM's Big SQL to query that data using SQL statements that are much like the ones we would use for any relation of data, including the data that we have in the Integrated Analytics System and Db2 Warehouse on Cloud. This and the federation capabilities that Big SQL offers dramatically simplifies data acquisition. Now we want to show you how we support a brand new tool that we're excited about. Since we launched last summer, the Data Science Experience has supported Jupyter and R for data analysis and visualization. In this week's update, we deeply integrated another great open source project called Apache Zeppelin. It's known for having great visualization support, advanced collaboration features, and is growing in popularity amongst the data science community. This is an example of Apache Zeppelin and the notebook we created through it to explore some of our data. Notice how wonderful and easy the data visualizations are. Now we want to walk you through the Jupyter notebook we created to explore our customer preference for stocks. We use notebooks to understand and explore data. To identify the features that have some predictive power. Ultimately, we're trying to assess what ultimately is driving customer stock preference. Here we did the analysis to identify the attributes of customers that are likely to purchase auto stocks. We used this understanding to build our machine learning model. For building machine learning models, we've always had tools integrated into the Data Science Experience. But sometimes you need to use tools you already invested in. Like our very own SPSS as well as SAS. Through new import feature, you can easily import those models created with those tools. This helps you avoid vendor lock-in, and simplify the development, training, deployment, and management of all your models. To build the models we used in app, we could have coded, but we prefer a visual experience. We used our customer profile data in the Integrated Analytic System. Used the Auto Data Preparation to cleanse our data. Choose the binary classification algorithms. Let the Data Science Experience evaluate between logistic regression and gradient boosted tree. It's doing the heavy work for us. As you can see here, the Data Science Experience generated performance metrics that show us that the gradient boosted tree is the best performing algorithm for the data we gave it. Once we save this model, it's automatically deployed and available for developers to use. Any application developer can take this endpoint and consume it like they would any other API inside of the apps they built. We've made training and creating machine learning models super simple. But what about the operations? A lot of companies are struggling to ensure their model performance remains high over time. In our financial adviser app, we know that customer data changes constantly, so we need to always monitor model performance and ensure that our models are retrained as is necessary. This is a dashboard that shows the performance of our models and lets our teams monitor and retrain those models so that they're always performing to our standards. So far we've been showing you the Data Science Experience available behind the firewall that we're using to build and train models. Through a new publish feature, you can build models and deploy them anywhere. In another environment, private, public, or anywhere else with just a few clicks. So here we're publishing our model to the Watson machine learning service. It happens to be in the IBM cloud. And also deeply integrated with our Data Science Experience. After publishing and switching to the Watson machine learning service, you can see that our stock affinity and model that we just published is there and ready for use. So this is incredibly important. I just want to say it again. The Data Science Experience allows you to train models behind your own firewall, take advantage of your proprietary and sensitive data, and then deploy those models wherever you want with ease. So summarize what we just showed you. First, IBM's Data Science Experience supports all teams. You saw how our data engineer populated our project with trusted data sets. Our data scientists developed, trained, and tested a machine learning model. Our developers used APIs to integrate machine learning into their apps. And how IT can use our Integrated Model Management dashboard to monitor and manage model performance. Second, we support all data. On premises, in the cloud, structured, unstructured, inside of your firewall, and outside of it. We help you bring analytics and governance to where your data is. Third, we support all tools. The data science tools that you depend on are readily available and deeply integrated. This includes capabilities from great partners like Hortonworks. And powerful tools like our very own IBM SPSS. And fourth, and finally, we support all deployments. You can build your models anywhere, and deploy them right next to where your data is. Whether that's in the public cloud, private cloud, or even on the world's most reliable transaction platform, IBM z. So see for yourself. Go to the Data Science Experience website, take us for a spin. And if you happen to be ready right now, our recently created Data Science Elite Team can help you get started and run experiments alongside you with no charge. Thank you very much. >> Thank you very much Daniel. It seems like a great time to get started. And thanks to Siva for taking us through it. Rob and I will be back in just a moment to add some perspective right after this. All right, once again joined by Rob Thomas. And Rob obviously we got a lot of information here. >> Yes, we've covered a lot of ground. >> This is intense. You got to break it down for me cause I think we zoom out and see the big picture. What better data science can deliver to a business? Why is this so important? I mean we've heard it through and through. >> Yeah, well, I heard it a couple times. But it starts with businesses have to embrace a data driven culture. And it is a change. And we need to make data accessible with the right tools in a collaborative culture because we've got diverse skill sets in every organization. But data driven companies succeed when data science tools are in the hands of everyone. And I think that's a new thought. I think most companies think just get your data scientist some tools, you'll be fine. This is about tools in the hands of everyone. I think the panel did a great job of describing about how we get to data science for all. Building a data culture, making it a part of your everyday operations, and the highlights of what Daniel just showed us, that's some pretty cool features for how organizations can get to this, which is you can see IBM's Data Science Experience, how that supports all teams. You saw data analysts, data scientists, application developer, IT staff, all working together. Second, you saw how we support all tools. And your choice of tools. So the most popular data science libraries integrated into one platform. And we saw some new capabilities that help companies avoid lock-in, where you can import existing models created from specialist tools like SPSS or others. And then deploy them and manage them inside of Data Science Experience. That's pretty interesting. And lastly, you see we continue to build on this best of open tools. Partnering with companies like H2O, Hortonworks, and others. Third, you can see how you use all data no matter where it lives. That's a key challenge every organization's going to face. Private, public, federating all data sources. We announced new integration with the Hortonworks data platform where we deploy machine learning models where your data resides. That's been a key theme. Analytics where the data is. And lastly, supporting all types of deployments. Deploy them in your Hadoop cluster. Deploy them in your Integrated Analytic System. Or deploy them in z, just to name a few. A lot of different options here. But look, don't believe anything I say. Go try it for yourself. Data Science Experience, anybody can use it. Go to datascience.ibm.com and look, if you want to start right now, we just created a team that we call Data Science Elite. These are the best data scientists in the world that will come sit down with you and co-create solutions, models, and prove out a proof of concept. >> Good stuff. Thank you Rob. So you might be asking what does an organization look like that embraces data science for all? And how could it transform your role? I'm going to head back to the office and check it out. Let's start with the perspective of the line of business. What's changed? Well, now you're starting to explore new business models. You've uncovered opportunities for new revenue sources and all that hidden data. And being disrupted is no longer keeping you up at night. As a data science leader, you're beginning to collaborate with a line of business to better understand and translate the objectives into the models that are being built. Your data scientists are also starting to collaborate with the less technical team members and analysts who are working closest to the business problem. And as a data scientist, you stop feeling like you're falling behind. Open source tools are keeping you current. You're also starting to operationalize the work that you do. And you get to do more of what you love. Explore data, build models, put your models into production, and create business impact. All in all, it's not a bad scenario. Thanks. All right. We are back and coming up next, oh this is a special time right now. Cause we got a great guest speaker. New York Magazine called him the spreadsheet psychic and number crunching prodigy who went from correctly forecasting baseball games to correctly forecasting presidential elections. He even invented a proprietary algorithm called PECOTA for predicting future performance by baseball players and teams. And his New York Times bestselling book, The Signal and the Noise was named by Amazon.com as the number one best non-fiction book of 2012. He's currently the Editor in Chief of the award winning website, FiveThirtyEight and appears on ESPN as an on air commentator. Big round of applause. My pleasure to welcome Nate Silver. >> Thank you. We met backstage. >> Yes. >> It feels weird to re-shake your hand, but you know, for the audience. >> I had to give the intense firm grip. >> Definitely. >> The ninja grip. So you and I have crossed paths kind of digitally in the past, which it really interesting, is I started my career at ESPN. And I started as a production assistant, then later back on air for sports technology. And I go to you to talk about sports because-- >> Yeah. >> Wow, has ESPN upped their game in terms of understanding the importance of data and analytics. And what it brings. Not just to MLB, but across the board. >> No, it's really infused into the way they present the broadcast. You'll have win probability on the bottom line. And they'll incorporate FiveThirtyEight metrics into how they cover college football for example. So, ESPN ... Sports is maybe the perfect, if you're a data scientist, like the perfect kind of test case. And the reason being that sports consists of problems that have rules. And have structure. And when problems have rules and structure, then it's a lot easier to work with. So it's a great way to kind of improve your skills as a data scientist. Of course, there are also important real world problems that are more open ended, and those present different types of challenges. But it's such a natural fit. The teams. Think about the teams playing the World Series tonight. The Dodgers and the Astros are both like very data driven, especially Houston. Golden State Warriors, the NBA Champions, extremely data driven. New England Patriots, relative to an NFL team, it's shifted a little bit, the NFL bar is lower. But the Patriots are certainly very analytical in how they make decisions. So, you can't talk about sports without talking about analytics. >> And I was going to save the baseball question for later. Cause we are moments away from game seven. >> Yeah. >> Is everyone else watching game seven? It's been an incredible series. Probably one of the best of all time. >> Yeah, I mean-- >> You have a prediction here? >> You can mention that too. So I don't have a prediction. FiveThirtyEight has the Dodgers with a 60% chance of winning. >> [Katie] LA Fans. >> So you have two teams that are about equal. But the Dodgers pitching staff is in better shape at the moment. The end of a seven game series. And they're at home. >> But the statistics behind the two teams is pretty incredible. >> Yeah. It's like the first World Series in I think 56 years or something where you have two 100 win teams facing one another. There have been a lot of parity in baseball for a lot of years. Not that many offensive overall juggernauts. But this year, and last year with the Cubs and the Indians too really. But this year, you have really spectacular teams in the World Series. It kind of is a showcase of modern baseball. Lots of home runs. Lots of strikeouts. >> [Katie] Lots of extra innings. >> Lots of extra innings. Good defense. Lots of pitching changes. So if you love the modern baseball game, it's been about the best example that you've had. If you like a little bit more contact, and fewer strikeouts, maybe not so much. But it's been a spectacular and very exciting World Series. It's amazing to talk. MLB is huge with analysis. I mean, hands down. But across the board, if you can provide a few examples. Because there's so many teams in front offices putting such an, just a heavy intensity on the analysis side. And where the teams are going. And if you could provide any specific examples of teams that have really blown your mind. Especially over the last year or two. Because every year it gets more exciting if you will. I mean, so a big thing in baseball is defensive shifts. So if you watch tonight, you'll probably see a couple of plays where if you're used to watching baseball, a guy makes really solid contact. And there's a fielder there that you don't think should be there. But that's really very data driven where you analyze where's this guy hit the ball. That part's not so hard. But also there's game theory involved. Because you have to adjust for the fact that he knows where you're positioning the defenders. He's trying therefore to make adjustments to his own swing and so that's been a major innovation in how baseball is played. You know, how bullpens are used too. Where teams have realized that actually having a guy, across all sports pretty much, realizing the importance of rest. And of fatigue. And that you can be the best pitcher in the world, but guess what? After four or five innings, you're probably not as good as a guy who has a fresh arm necessarily. So I mean, it really is like, these are not subtle things anymore. It's not just oh, on base percentage is valuable. It really effects kind of every strategic decision in baseball. The NBA, if you watch an NBA game tonight, see how many three point shots are taken. That's in part because of data. And teams realizing hey, three points is worth more than two, once you're more than about five feet from the basket, the shooting percentage gets really flat. And so it's revolutionary, right? Like teams that will shoot almost half their shots from the three point range nowadays. Larry Bird, who wound up being one of the greatest three point shooters of all time, took only eight three pointers his first year in the NBA. It's quite noticeable if you watch baseball or basketball in particular. >> Not to focus too much on sports. One final question. In terms of Major League Soccer, and now in NFL, we're having the analysis and having wearables where it can now showcase if they wanted to on screen, heart rate and breathing and how much exertion. How much data is too much data? And when does it ruin the sport? >> So, I don't think, I mean, again, it goes sport by sport a little bit. I think in basketball you actually have a more exciting game. I think the game is more open now. You have more three pointers. You have guys getting higher assist totals. But you know, I don't know. I'm not one of those people who thinks look, if you love baseball or basketball, and you go in to work for the Astros, the Yankees or the Knicks, they probably need some help, right? You really have to be passionate about that sport. Because it's all based on what questions am I asking? As I'm a fan or I guess an employee of the team. Or a player watching the game. And there isn't really any substitute I don't think for the insight and intuition that a curious human has to kind of ask the right questions. So we can talk at great length about what tools do you then apply when you have those questions, but that still comes from people. I don't think machine learning could help with what questions do I want to ask of the data. It might help you get the answers. >> If you have a mid-fielder in a soccer game though, not exerting, only 80%, and you're seeing that on a screen as a fan, and you're saying could that person get fired at the end of the day? One day, with the data? >> So we found that actually some in soccer in particular, some of the better players are actually more still. So Leo Messi, maybe the best player in the world, doesn't move as much as other soccer players do. And the reason being that A) he kind of knows how to position himself in the first place. B) he realizes that you make a run, and you're out of position. That's quite fatiguing. And particularly soccer, like basketball, is a sport where it's incredibly fatiguing. And so, sometimes the guys who conserve their energy, that kind of old school mentality, you have to hustle at every moment. That is not helpful to the team if you're hustling on an irrelevant play. And therefore, on a critical play, can't get back on defense, for example. >> Sports, but also data is moving exponentially as we're just speaking about today. Tech, healthcare, every different industry. Is there any particular that's a favorite of yours to cover? And I imagine they're all different as well. >> I mean, I do like sports. We cover a lot of politics too. Which is different. I mean in politics I think people aren't intuitively as data driven as they might be in sports for example. It's impressive to follow the breakthroughs in artificial intelligence. It started out just as kind of playing games and playing chess and poker and Go and things like that. But you really have seen a lot of breakthroughs in the last couple of years. But yeah, it's kind of infused into everything really. >> You're known for your work in politics though. Especially presidential campaigns. >> Yeah. >> This year, in particular. Was it insanely challenging? What was the most notable thing that came out of any of your predictions? >> I mean, in some ways, looking at the polling was the easiest lens to look at it. So I think there's kind of a myth that last year's result was a big shock and it wasn't really. If you did the modeling in the right way, then you realized that number one, polls have a margin of error. And so when a candidate has a three point lead, that's not particularly safe. Number two, the outcome between different states is correlated. Meaning that it's not that much of a surprise that Clinton lost Wisconsin and Michigan and Pennsylvania and Ohio. You know I'm from Michigan. Have friends from all those states. Kind of the same types of people in those states. Those outcomes are all correlated. So what people thought was a big upset for the polls I think was an example of how data science done carefully and correctly where you understand probabilities, understand correlations. Our model gave Trump a 30% chance of winning. Others models gave him a 1% chance. And so that was interesting in that it showed that number one, that modeling strategies and skill do matter quite a lot. When you have someone saying 30% versus 1%. I mean, that's a very very big spread. And number two, that these aren't like solved problems necessarily. Although again, the problem with elections is that you only have one election every four years. So I can be very confident that I have a better model. Even one year of data doesn't really prove very much. Even five or 10 years doesn't really prove very much. And so, being aware of the limitations to some extent intrinsically in elections when you only get one kind of new training example every four years, there's not really any way around that. There are ways to be more robust to sparce data environments. But if you're identifying different types of business problems to solve, figuring out what's a solvable problem where I can add value with data science is a really key part of what you're doing. >> You're such a leader in this space. In data and analysis. It would be interesting to kind of peek back the curtain, understand how you operate but also how large is your team? How you're putting together information. How quickly you're putting it out. Cause I think in this right now world where everybody wants things instantly-- >> Yeah. >> There's also, you want to be first too in the world of journalism. But you don't want to be inaccurate because that's your credibility. >> We talked about this before, right? I think on average, speed is a little bit overrated in journalism. >> [Katie] I think it's a big problem in journalism. >> Yeah. >> Especially in the tech world. You have to be first. You have to be first. And it's just pumping out, pumping out. And there's got to be more time spent on stories if I can speak subjectively. >> Yeah, for sure. But at the same time, we are reacting to the news. And so we have people that come in, we hire most of our people actually from journalism. >> [Katie] How many people do you have on your team? >> About 35. But, if you get someone who comes in from an academic track for example, they might be surprised at how fast journalism is. That even though we might be slower than the average website, the fact that there's a tragic event in New York, are there things we have to say about that? A candidate drops out of the presidential race, are things we have to say about that. In periods ranging from minutes to days as opposed to kind of weeks to months to years in the academic world. The corporate world moves faster. What is a little different about journalism is that you are expected to have more precision where people notice when you make a mistake. In corporations, you have maybe less transparency. If you make 10 investments and seven of them turn out well, then you'll get a lot of profit from that, right? In journalism, it's a little different. If you make kind of seven predictions or say seven things, and seven of them are very accurate and three of them aren't, you'll still get criticized a lot for the three. Just because that's kind of the way that journalism is. And so the kind of combination of needing, not having that much tolerance for mistakes, but also needing to be fast. That is tricky. And I criticize other journalists sometimes including for not being data driven enough, but the best excuse any journalist has, this is happening really fast and it's my job to kind of figure out in real time what's going on and provide useful information to the readers. And that's really difficult. Especially in a world where literally, I'll probably get off the stage and check my phone and who knows what President Trump will have tweeted or what things will have happened. But it really is a kind of 24/7. >> Well because it's 24/7 with FiveThirtyEight, one of the most well known sites for data, are you feeling micromanagey on your people? Because you do have to hit this balance. You can't have something come out four or five days later. >> Yeah, I'm not -- >> Are you overseeing everything? >> I'm not by nature a micromanager. And so you try to hire well. You try and let people make mistakes. And the flip side of this is that if a news organization that never had any mistakes, never had any corrections, that's raw, right? You have to have some tolerance for error because you are trying to decide things in real time. And figure things out. I think transparency's a big part of that. Say here's what we think, and here's why we think it. If we have a model to say it's not just the final number, here's a lot of detail about how that's calculated. In some case we release the code and the raw data. Sometimes we don't because there's a proprietary advantage. But quite often we're saying we want you to trust us and it's so important that you trust us, here's the model. Go play around with it yourself. Here's the data. And that's also I think an important value. >> That speaks to open source. And your perspective on that in general. >> Yeah, I mean, look, I'm a big fan of open source. I worry that I think sometimes the trends are a little bit away from open source. But by the way, one thing that happens when you share your data or you share your thinking at least in lieu of the data, and you can definitely do both is that readers will catch embarrassing mistakes that you made. By the way, even having open sourceness within your team, I mean we have editors and copy editors who often save you from really embarrassing mistakes. And by the way, it's not necessarily people who have a training in data science. I would guess that of our 35 people, maybe only five to 10 have a kind of formal background in what you would call data science. >> [Katie] I think that speaks to the theme here. >> Yeah. >> [Katie] That everybody's kind of got to be data literate. >> But yeah, it is like you have a good intuition. You have a good BS detector basically. And you have a good intuition for hey, this looks a little bit out of line to me. And sometimes that can be based on domain knowledge, right? We have one of our copy editors, she's a big college football fan. And we had an algorithm we released that tries to predict what the human being selection committee will do, and she was like, why is LSU rated so high? Cause I know that LSU sucks this year. And we looked at it, and she was right. There was a bug where it had forgotten to account for their last game where they lost to Troy or something and so -- >> That also speaks to the human element as well. >> It does. In general as a rule, if you're designing a kind of regression based model, it's different in machine learning where you have more, when you kind of build in the tolerance for error. But if you're trying to do something more precise, then so much of it is just debugging. It's saying that looks wrong to me. And I'm going to investigate that. And sometimes it's not wrong. Sometimes your model actually has an insight that you didn't have yourself. But fairly often, it is. And I think kind of what you learn is like, hey if there's something that bothers me, I want to go investigate that now and debug that now. Because the last thing you want is where all of a sudden, the answer you're putting out there in the world hinges on a mistake that you made. Cause you never know if you have so to speak, 1,000 lines of code and they all perform something differently. You never know when you get in a weird edge case where this one decision you made winds up being the difference between your having a good forecast and a bad one. In a defensible position and a indefensible one. So we definitely are quite diligent and careful. But it's also kind of knowing like, hey, where is an approximation good enough and where do I need more precision? Cause you could also drive yourself crazy in the other direction where you know, it doesn't matter if the answer is 91.2 versus 90. And so you can kind of go 91.2, three, four and it's like kind of A) false precision and B) not a good use of your time. So that's where I do still spend a lot of time is thinking about which problems are "solvable" or approachable with data and which ones aren't. And when they're not by the way, you're still allowed to report on them. We are a news organization so we do traditional reporting as well. And then kind of figuring out when do you need precision versus when is being pointed in the right direction good enough? >> I would love to get inside your brain and see how you operate on just like an everyday walking to Walgreens movement. It's like oh, if I cross the street in .2-- >> It's not, I mean-- >> Is it like maddening in there? >> No, not really. I mean, I'm like-- >> This is an honest question. >> If I'm looking for airfares, I'm a little more careful. But no, part of it's like you don't want to waste time on unimportant decisions, right? I will sometimes, if I can't decide what to eat at a restaurant, I'll flip a coin. If the chicken and the pasta both sound really good-- >> That's not high tech Nate. We want better. >> But that's the point, right? It's like both the chicken and the pasta are going to be really darn good, right? So I'm not going to waste my time trying to figure it out. I'm just going to have an arbitrary way to decide. >> Serious and business, how organizations in the last three to five years have just evolved with this data boom. How are you seeing it as from a consultant point of view? Do you think it's an exciting time? Do you think it's a you must act now time? >> I mean, we do know that you definitely see a lot of talent among the younger generation now. That so FiveThirtyEight has been at ESPN for four years now. And man, the quality of the interns we get has improved so much in four years. The quality of the kind of young hires that we make straight out of college has improved so much in four years. So you definitely do see a younger generation for which this is just part of their bloodstream and part of their DNA. And also, particular fields that we're interested in. So we're interested in people who have both a data and a journalism background. We're interested in people who have a visualization and a coding background. A lot of what we do is very much interactive graphics and so forth. And so we do see those skill sets coming into play a lot more. And so the kind of shortage of talent that had I think frankly been a problem for a long time, I'm optimistic based on the young people in our office, it's a little anecdotal but you can tell that there are so many more programs that are kind of teaching students the right set of skills that maybe weren't taught as much a few years ago. >> But when you're seeing these big organizations, ESPN as perfect example, moving more towards data and analytics than ever before. >> Yeah. >> You would say that's obviously true. >> Oh for sure. >> If you're not moving that direction, you're going to fall behind quickly. >> Yeah and the thing is, if you read my book or I guess people have a copy of the book. In some ways it's saying hey, there are lot of ways to screw up when you're using data. And we've built bad models. We've had models that were bad and got good results. Good models that got bad results and everything else. But the point is that the reason to be out in front of the problem is so you give yourself more runway to make errors and mistakes. And to learn kind of what works and what doesn't and which people to put on the problem. I sometimes do worry that a company says oh we need data. And everyone kind of agrees on that now. We need data science. Then they have some big test case. And they have a failure. And they maybe have a failure because they didn't know really how to use it well enough. But learning from that and iterating on that. And so by the time that you're on the third generation of kind of a problem that you're trying to solve, and you're watching everyone else make the mistake that you made five years ago, I mean, that's really powerful. But that doesn't mean that getting invested in it now, getting invested both in technology and the human capital side is important. >> Final question for you as we run out of time. 2018 beyond, what is your biggest project in terms of data gathering that you're working on? >> There's a midterm election coming up. That's a big thing for us. We're also doing a lot of work with NBA data. So for four years now, the NBA has been collecting player tracking data. So they have 3D cameras in every arena. So they can actually kind of quantify for example how fast a fast break is, for example. Or literally where a player is and where the ball is. For every NBA game now for the past four or five years. And there hasn't really been an overall metric of player value that's taken advantage of that. The teams do it. But in the NBA, the teams are a little bit ahead of journalists and analysts. So we're trying to have a really truly next generation stat. It's a lot of data. Sometimes I now more oversee things than I once did myself. And so you're parsing through many, many, many lines of code. But yeah, so we hope to have that out at some point in the next few months. >> Anything you've personally been passionate about that you've wanted to work on and kind of solve? >> I mean, the NBA thing, I am a pretty big basketball fan. >> You can do better than that. Come on, I want something real personal that you're like I got to crunch the numbers. >> You know, we tried to figure out where the best burrito in America was a few years ago. >> I'm going to end it there. >> Okay. >> Nate, thank you so much for joining us. It's been an absolute pleasure. Thank you. >> Cool, thank you. >> I thought we were going to chat World Series, you know. Burritos, important. I want to thank everybody here in our audience. Let's give him a big round of applause. >> [Nate] Thank you everyone. >> Perfect way to end the day. And for a replay of today's program, just head on over to ibm.com/dsforall. I'm Katie Linendoll. And this has been Data Science for All: It's a Whole New Game. Test one, two. One, two, three. Hi guys, I just want to quickly let you know as you're exiting. A few heads up. Downstairs right now there's going to be a meet and greet with Nate. And we're going to be doing that with clients and customers who are interested. So I would recommend before the game starts, and you lose Nate, head on downstairs. And also the gallery is open until eight p.m. with demos and activations. And tomorrow, make sure to come back too. Because we have exciting stuff. I'll be joining you as your host. And we're kicking off at nine a.m. So bye everybody, thank you so much. >> [Announcer] Ladies and gentlemen, thank you for attending this evening's webcast. If you are not attending all cloud and cognitive summit tomorrow, we ask that you recycle your name badge at the registration desk. Thank you. Also, please note there are two exits on the back of the room on either side of the room. Have a good evening. Ladies and gentlemen, the meet and greet will be on stage. Thank you.

>> SiliconANGLE Media presents theCUBE! Covering the Alibaba Cloud annual conference. Brought to you by Intel. Now, here's John Furrier... >> Hello everyone, welcome to exclusive coverage with SiliconANGLE, Wikibon, and theCUBE here in Hangzhou, China for Alibaba Cloud's annual event here in Cloud City, the whole town is a Cloud. This is their event with developers, music festivals, and again, theCUBE coverage. Our next guest is Dhiraj Mallick, who is the Vice President of the Data Center Group, and the General Manager of Innovation, Pathfinding, and Architecture Group. That's a mouthful. Basically the CTO of the Data Center Group, trying to figure out the next big thing. >> That's right, John. >> Thanks for spending the time. >> It's my pleasure. >> We're here in China, it's-- You know in the U.S., we're looking at China, and we say okay, the fourth largest Cloud, Alibaba Cloud? >> Yes. >> Going outside of Mainland China, going global. You guys are strategic partners with them. >> Yes. >> They need a lot of compute, they need a lot of technology. Is this the path that you're finding for Intel? >> Yeah, so we've been collaborators with Alibaba for over 10 years, and we view them as a very strategic partner. They're one of the Super Seven, which is our top seven Cloud providers, and certainly in China, they're a very relevant customer for many years. We engage with them on a variety of fronts. On the technology side, we engage with them on what their key pinpoints are, what is the problems they want to be solving three to five years out, and then we co-develop, or co-architect solutions with them. >> So, I want to get your take on the event here in China, and how it relates to the global landscape, because I, it's my first time here, and I was taken back by the booth. I walked through Alibaba's booth, and obviously Jack Ma is inspirational. Steve Jobs like the culture, and artistry and science coming together, but I walked through the booth, it's almost too good to be true. They've got Quantum Computing, a Patent Wall, they've got Hybrid Cloud, they got security, they have IoT examples with The City Brain, a lot of great tech here at Alibaba Cloud. >> So I think the technologies that they're investing in are very, very impressive. Most cloud companies are probably not as far along as them, and looking at such a broad range of technologies, the Brain Project is really exciting, because it's going to be the Nexus of smart cities, both in China, as well as globally. The second thing that's very interesting is their research and investments in Quantum. While Quantum is not here today, it's certainly on the frontier, and Intel also has significant investments in sort of unpacking where Quantum will go, and what promises it offers to address. >> What I find interesting is that also hearing the positioning of, I kind of squint through the positioning, they're almost talking Cloud-native, DevOps, but they have all this goodness under the hood, and they're kind of talking IT-transitioning to Data Technology. Everything's about data to these guys, not just collecting data, using data with software. Now, that's really critical, because isn't that software-defined, data-driven is a hot trend? >> Yes, software-defined and data-driven is a very hot trend, in fact at Intel our CEO and us all believe that we've entered the data economy, and that the explosion in data is, and the thirst for analyzing that data to be able to drive smart business analytics is really the key to this digital revolution. I was reading an industry report by one of the analysts that said by 2019 there would have been over 100 billion dollars spent on business intelligence. And so, the real key is this data economy. >> The intersection of things, and even industrial internet, IIot, Industrial Iot, with artificial intelligence AI, intelligence Intel inside that word, interesting play on words-- >> Yes. >> Is coming together, and we've covered what you guys were doing on Mobile World Congress this year, where 5G was clearly an end-to-end architecture. You got FPGAs, all this goodness here going on. So that's 5G, and that's going to fuel a lot of IoT if you think of it like that way, but now AI. >> Yes. >> It's Software. How does that connect? Because that's the path we see forward on the Wikibon analyst side, we see software eating the world, but data eating software. And now you got 5G creating more data. >> Yeah, so the way we look at it at Intel is, we have data-center technologies that are fueled by the growth at the Edge by IoT devices, because they're creating demand for more processing capability to be able to unpack and analyze that information, and it's a self-fulfilling circle. We call it the virtual cycle of growth, because the data center feeds IoT demand and then IoT feeds the data center. And so it's the combination of those. What 5G does, is 5G forms the connectivity fabric between the data center and the Edge. It allows data to be pre-positioned at the correct places in the network, so that you minimize latencies through the network, and can process or do the analytics on it as quickly as you possibly can. >> So we were talking before we came on camera about Jack Ma, they call him Jackie Ma here, keynote being very inspirational, and talking moving to a new industrial era, a digital economy, all that good stuff, very, very inspirational. Let's translate that into the data center transformation, because we're seeing the data center and the Cloud with Hybrid Cloud become really critical to support what you were just talking about which is, how do you put it all together? It sounds so easy, but it really is difficult. >> It is, and so our vision is that in order to be able to fulfill this data economy, we will need to have five key innovations in the data center. The first innovation, in no particular order, is that the data center will be frictionless. And what I mean by frictionless, is that there will be zero to low latencies in order to provide that real-time experience at the Edge. So latency is extremely critical, and the way we believe that that can be achieved is by moving from copper to light. And Intel has significant investments in leadership products and silicon photonics that will enable switches to be based on photonics. It'll enable CPUs, and server hosts to be based on light. So we believe that light is a critical aspect to this success. The second aspect of frictionless is the need for liquid cooling and that was in the keynotes from Simon Hu this morning, that the liquid cooling is going to be essential to be able to enable a lot more horsepower in these data centers to be able to handle the volume of data that's coming. >> So you guys obviously with the photonics and the liquid cooling, you guys have been working on this in your labs for a long time, it's great R&D, but you need the connective tissue because with 5G you're now talking about a ubiquitous RF cloud, powering autonomous vehicles. We're seeing the Brain Project here, ET Brain, the City Brain-- >> Yes. >> Which is essentially IoT and big data being a big application that they're showcasing. What's the connective tissue? How does that work, from the data center, to the Edge? What's Intel's position? How do you see it? And what's going to unfold in front of our eyes? >> Yeah, so two things, so number one, I believe that the data center is boundary-less. It's not based on four physical walls. It's a connected link between the data center, and all the Edge devices that you called IoT. In order to fulfill this, you have to have 5G technology. We're invested in Silicon, in radio technologies, as well as in driving the 5G industry in consortia, to be able to bring 5G solutions to market. We think that 5G, as well as a tiered architecture between the Edge to the center, where you do some processing at the Edge, the radio stations, some in intermediate data centers, and then some in the back end Cloud data center, is what's going to be essential, and Intel has significant investments, both in developing this distributed hierarchical architecture, as well as in 5G. >> That's a great point. I want to just unpack that, and double-click on it a little bit, because you mentioned data at the Edge, and you also said earlier, low latency. Okay, a lot of people have been talking about, it costs you speed and time to move data around. So there's no real one general architecturing, where you have to kind of decide the architecture for the use case. >> Yes. >> So, the beauty is in the eye of the beholder, whoever has the workloads or the equipment. >> Yes. >> How do you look at that, because now you're thinking about, if I don't want to move data around, maybe you shouldn't, maybe you want to move data around. How does that fit with the Cloud of model, because we're seeing Cloud being a great use case for IoT in one instance, and maybe not in another. How do you think about that? How should practitioners think about the data architecture? >> Yeah, so our vision is that the Cloud changes from a centralized Cloud, to a distributed Cloud, and is amorphoused between the Edge where the IoT devices are, and the backend, and the way to think about it perhaps, is to say that storage as people have envisioned it, as being centralized, that paradigm has to change, and storage has to become distributed, such that data is available at different points in the network, and my vision is that you don't want to move data around, you want to minimize data movement for most use cases, and you want to have it pre-positioned on the 5G network, and you want to move the compute to the data, that's more energy-efficient. >> So I got to ask you, as someone who's doing the path-finding, which is the future path for Intel, and innovation and architecture. I was talking with some practitioners recently at another event, and trying to find someone, because I don't speak Chinese very well. But they asked me the same question. It matters what's in my Cloud. And what they mean by their Cloud, either on-premise private Cloud that they're putting together, operating model of their business, now going Cloud-like. But also as they pick their Cloud provider, they want to have multi-Cloud, and so what's in their Cloud, and their Cloud provider's matters. You guys are the inside of the Cloud across many spectrums, Intel. >> Yes. >> How should a customer think about that question? What's in my Cloud? Why should it matter, and it should matter. What's your take on that, and what should they look for? >> Yeah, so my take is that for years we've had the debate of whether it's public Cloud, or private Cloud, or on-prem Cloud. Our view is that the world is Hybrid, which is why we are big supporters of Alibaba, and the Hybrid Cloud movement, and as such, if it's Hybrid, it sort of suggests that the end state is that there'll be about an equal amount of applications that run on public versus private, and so I think the number of applications have an affinity to move into the public Cloud, like mail, and then there's other applications that you might care more about the compliance and security that you would say have an affinity to being on-prem. >> Also you mentioned that there's no walls, it's boundary-less in the data center. Okay, there's no door, there's no mote, you can't put a firewall on that door, unlimited access surface area for security. Obviously security hacks are big. We found out today that Israel had hacked, and notified the NSA. Hacking is a huge problem. Equifax is going to be another one. How should customers protect themselves? >> It's a very fair question John. This is one of the side-effects of saying that the data center will be boundary-less. We now have to have security technologies that can, we've effectively expanded the attacks of security in a significant way, but I don't think the answer is to say we need to move backwards and not adopt this boundary-less Cloud. I think we want to adopt it, and we want to develop technologies. So at Intel, we are developing multiple isolation technologies that allow different VM and container tenants to be isolated from other tenants. >> And this was your point earlier, making the device more intelligent, whether that's more on-board memory, and more chips. >> Yes. >> That's what you were kind of referring to, is that right? >> That's correct. >> Okay great, so I want to get one kind of off-the-wall question, since I have you on here. It's just a brain trust here from Intel, which it's great to have him here. Distributed computing has been around for awhile, we know all about that. Network effects, distributed computing, the computer industry. But now we're seeing a trend with decentralization. Blockchain is one shining example. Russia just banned cryptocurrency. This poses a architectural challenge. What's your thoughts on the decentralization, and distributed architectures that are emerging? Opportunity is scary. How should customers think about decentralization? >> Well certainly there's a security challenge, as we just spoke, related to this. But I think the computer industry has oscillated, depending on the era and the needs between centralized and decentralized a number of times now. And we're going through an era where decentralization makes sense, because we expect 30 to 50 billion devices at the Edge, and so you can't handle that with a centralized model, primarily due to three reasons, number one, just moving that volume of data would be very expensive to do over the network. Second there'll be a number of applications that are latency-sensitive. And third, you might care about data federation, and crossing country boundaries in a number of cases. So I think for the use case that we have with IoT, we have to adopt decentralized and distributed. >> So, if The Brain is processing and data, and you've got plenty of it at Intel with more compute power, what's the central nervous system, the metadata? >> Well, actually look at the central nervous system as the 5G distributed network that enables the end-points, or the nerve endings if you will, to be connected to the spinal cord. >> Okay so a final question for you, I really appreciate you spending the time. >> Sure, it's been a pleasure. >> Intel's been a wave company in its generation, and obviously Moore's law, it's not well documented. It seems that Moore's law is every year some journalist claims Moore's law is dead, and that it never goes away, so we expect more and more innovation coming from Intel. You guys have surfed many waves. In your opinion, what waves are coming? Because it feels like the waves are big now, but a lot of people think that there's bigger waves coming. That the big wave set is coming in. What's the technology wave that you're looking at from a path-finding, innovation standpoint, that customers should look for, maybe prepare for. It could be further out coming in. What's the big wave coming in, obviously AI was seeing these things. What's your focus on that? >> So, a number of them. I think, you know distributed computing is not a solved problem yet. But certainly it needs to be solved to be able to address these end-point challenges. Another great example I think, is around visual computing. So in the past, most of the type of data that people handled, was textual. But that's moving to visual very rapidly, and there's so many examples. You brought up the City Brain Project as an example. But video and analyzing images, requires a different kind of art. Different compression techniques. If a human doesn't need to see it, you perhaps don't have to have as high a resolution, and so there's a number of ships in the assumption space. And so I think for me, visual computing is a great opportunity, as well as a wave, that's coming at us. >> And the software too. So the final question, final, final question. Alibaba here, are connecting the dots. You can see where it's going. How do you see the Cloud service provider opportunity, because obviously they're a Cloud service provider on paper, but they're big, they're a Native Cloud now, like with the big guys like Amazon, Google, Microsoft. But we're seeing an emergence of new class of Cloud service provider. Certainly our research is showing that what was a very thin neck in the power laws, now expanding into a much bigger range, where VARs and value-edited software developers are going to start doing their own Cloud-like solutions with the Native Clouds, because they need horizontally scalable data infrastructure, connective tissue, and Edge devices from Intel, but they're going to provide software expertise that's vertically specialized, whether it's traffic, IoT, or oil and gas, or financial, Fintech. The specialism of application developers combined with horizontally scalable Cloud, it seems like a renaissance in the Cloud service provider market. Do you see that as well, and how should the industry think about this potential renaissance? >> So I think there's two possibilities. One is for the vast majority of functions that people run in the public Cloud, I think one possibility is that there's a consolidation amongst a few players. But I think your point's a very good one. That they are specialized services that companies are able to provide, where they're able to carve out a niche, and become a Cloud provider for that particular set of functions, as well as there's a second reason that motivates regional Cloud providers to succeed, again, because of data federation requirements, as well as local proximal, proximity to the end-points. I think these two phenomena are likely to drive the emergence of regional Clouds, as well as specialized Clouds, like you described to perform certain functions. >> And potentially a new kind of ecosystem development. >> Yes. >> And this is, then you guys are all about ecosystems, so is Alibaba. >> That's right. >> Dhiraj, thanks so much for coming on theCUBE, this is exclusive CUBE coverage with SiliconANGLE, and Wikibon here in China with Intel's booth here. Talking about AI, and the future of the data center and Cloud. I'm John Furrier, thanks for watching.

>> Speaker: Live from Las Vegas. It's the Cube. Covering VMworld 2017. Brought to you by VMware and its ecosystem partners. >> Yeah we're here live the Cube coverage at VMworld 2017. Behind us is the floor of the VMvillage. I'm John Furrier with Dave Vellante. Our next two guest Beth Phalen who's the President and General Manager of Data Protection Division at Dell EMC and Yanbing Li who's the Senior Vice President General Management with Storage and Availability at VMware, vSAN, all the greatness; Welcome back to the Cube. Great to see you guys. >> Yeah, great to see you. >> Got the heavy hitters here, data protection, AWS lot of great relationships synergies happening. >> Yeah. >> Give us the update. >> Yeah well go ahead yeah. >> We've been working together for a long time but recently we've really amped it up to the next level. Great discussions around enabling data protection for vSAN and as announced this week you know with Dell EMC will be first vendor to have data protection for VMware cloud on AWS. So it's a really exciting time to be here and I've been in this business for a long time. This is the best VMworld that I've seen so far and so it's just really great to be here with Yanbing. >> It's been very cohesive, I want to just stay on that for a second. This is the big milestone for VMware. >> It is. >> To have this shipping of the general availability especially with on the heels of the vCloud Air and all that controversy. Andy Jassy's on stage from Amazon web services. >> Yeah. >> Really kind of looking right at the audience and saying we got your back, this is a real deal, and the bridge to the future. I'm paraphrasing, he didn't say those exact words. >> Yeah yeah yeah. >> How do you get that data protection? Because that data protection in the cloud is hard. >> Yeah, well the nice thing is that since we've got all of our data protection running in a cloud environment now we could then use that to build the connections with VMC. So we had Data Domain Virtual Edition running, we have Data Protection Suite running in the cloud. So people can use the same technology they used on prem but now in AWS in conjunction with VMC. >> So you kind have hyper converged infrastructure meets cloud data protection. Yanbing, what is the difference? I mean what's the requirement of hyper converged infrastructure data protection? How does it differ from traditional storage and how is it evolving? >> Ah, great questions you know Beth and I we've known each other for quite a few years. I have to say our relationship hasn't been, you know, this close is and it's getting closer and closer. So coming back to your question in terms of hyper converged infrastructure. We're seeing two fundamental shifts around data protection. One is, the blurring of the boundary between backup and DR and these two really coming together as unified data protection. I think there has been a lot of discussion around this for a long time but this become even more compelling; now we talk about hyper converged infrastructure where you know our customers they so enjoy the benefit of having compute and storage combined together in a common management experience, they're looking for the same for data protection. So we're really seeing customers want to see data protection as a feature of hyper converged, as a capability that's part of that rather than yet another silo they have to manage separately. You know they want policy that manage storage, compute, and backup and DR altogether. So that's why you know that's really drive our partnership so much closer. >> You know it's interesting many of the clients that we've worked with over the years they'll have a backup strategy but they don't really have a DR strategy and they sleep with one eye open at night and they're afraid to go to the board because it's so expensive, it's expensive insurance. So you're seeing that there, sounds like they're blending those 2 together kind of killing 2 birds with one stone. Are there trade offs or things that customers should think about in that regard? How do they sort of go from where they are today which is sort of a backup bolt on to that integrated DR and backup? >> I think one of the key is the technology that we're leveraging now and we leverage something that has like CDP continuous data protection you can use that one to have data path to the secondary storage and you can use that same code to also initiate disaster recovery with near 0 RPO and RTO. So another thing that we announced this week is with our DPS for apps next edition that we now have hypervisor direct back up and what that means is that we're integrated directly with ESX and we are leveraging ProtectPoint through VM's to move data to data domain. That same technology is also leverage within RecoverPoint through VM's and so you can see the engine, the internal engine of the data movements, can be applied both to disaster recovery and to back up with different windows of RTO and RPO. >> I'm glad you said near 0 RPO causes no such thing as 0 RPO but you're seeing, more pressure to get as close to 0 as possible. What's driving that pressure and how are you meeting it? >> Well I think with all of us we know that an industry customers are expecting 24 by, you know 24 by 7 up time right. So they have many many applications that they need to have the confidence that if it does go down for any reason they're going to be able to bring it back up within minutes or hours not days. So that's really the drive for continuous availability. Getting as close to that as possible. >> If I may one more John, the challenge in data protection has always been it's, it's largely been a one size fits all and it's either I'm either under protected or I'm spending and breaking the bank. So are you able to through your technology and process improvements improve the level of granularity for different workloads that require different service levels. >> Two things come to mind, One, we're seeing more and more interesting customers integrating data protection directlywith their applications. Whether it SQL or Oracle and or the VM itself. So that's one thing. So we can custom the data protection to particular application and then on the second piece of that is where the different interfaces that VM offers we're able to do either V80P level integration or more fine grained integration like we do with CheckPoint through VM. So we are getting to the point that we can make different choices either application specific or something that is fine tuned based on the level of mission critical capabilities that application requires. >> I will get you guys perspective just a high level ballistic view for a second. We're seeing convergence of two worlds. The cloud native world that have no walls, have no perimeters they operate in a mindset of there's a security holes everywhere. Then the protections hard. >> They think of a differently. >> Yeah On prem the traditional methods, how are those coming together? Because you have customers that run VMware and do stuff with data protection and then one of them VMware in the cloud. What's different, what do customers need to know that are we on either side of that equation? If I'm on prem and I now want to use VMware in the cloud on AWS. How does data protection fit in that? Is it the same, is there tweaks, how they think about it? >> You want to answer that? >> In terms of on prem or VMware in AWS you know a big value prop is reading at the consistency in the operating model. I'm sure you have heard about this a million times said. >> Yes, talking about it all week. >> All week long. From data protection we're trying to do exactly the same. So for example VMware cloud on AWS, the very first data protection that we certify on that platform is from [Vast 00:07:39] organization is Avamar networker being the first set of solution certified and our customers definitely love the continuity of I already have the experience and licensing associated with my own prem protection solution and they want to carry that forward in today's cloud. >> So same operating module, so from the customers perspective I've been doing it this way >> Exactly. >> With VMware and Dell Data Protection, now it's the same in the cloud. No change in. >> Yeah I mean I think that's really the beauty of it, even with DDVE I mean you can have applications or you can do through different; You know you can have application in the cloud as well as another level of protection of your secondary storage. >> I think some of the changes probably not necessary. So RPD model consistency, Dave we touch upon, hyper convergence is driving a lot of functionality into a single control plate as opposed to these different silos and you know we would like to see that happen in the cloud as well and along that line you know best organization and my organizing are really looking at how we viewed the best next generation integrated technology that truly leverages the strengths of both organizations. >> That's simple and easy to use. >> Simple, easy to use, policy base, you know turn key solutions, so this is, you know what we're doing something pretty innovative by truly bring our engineering together and try to boost our next generation solution. >> Since the synergies that Michael was talking about when we interviewed Michael yesterday he's like look, the synergies are well beyond its expectations. Just it seems to be flowing nicely in the culture. When EMC had the federation there was always kind of like an interesting but now things are flowing differently. It seems to be smoother you guys. >> They are. >> Every action. >> I totally agree with what you said. I mean it feels different and I think as we go forward we have even more opportunities but we're not even a year into it and there was a distinct difference in terms of recognition around the joint opportunity and like you said the smoothness of the conversation I think is >> It's clear, it's clarity. >> It's really helpful. >> Well also you know, the rising tide floats all boats, well VMware stock as gone like this. >> It makes us all happy. >> Its got a nice slope to it. >> I definitely want to hackle Beth on that and the type of collaboration we're seeing between our two organizations, might be you is actually having multiple touch point into Dell and Dell EMC organization whether it's our VxRail and you know the vSAN based collaboration or the data protection angle and we're really seeing that happen across different functions. So we are starting from go to market collaboration you know how we provide the best set of solutions to our customers in joint go to market effort. vSAN is gaining a lot of free print in mission critical workloads and a critical requirement is data protection. So so we're doing a lot of joint solution, joint selling together. And really in the next step is that joint engineering effort leveraging the best of both worlds to build next generation products that's optimized for hyper converged, that's optimized for the cloud. >> For the software defined data centers. >> If I dial back a decade let's say as virtualization generally in VMware specifically saw its ascendancy, data protection totally changed. For a number of reasons, you had less physical resources but backup was still very resource intensive application and so; That's really where Avarmar came before. He walked the floor, back up and data protection is exploding again. It's like the hottest area. So two part question. Why is that and then how does Dell EMC with you know its large portfolio, its big install base, how do you maintain competitiveness with all that new emerging innovation? >> Yeah well I think the first question and I want to hear your answer too but what I would say is because the industry is changing so dramatically it's requiring data protection to change just as dramatically. >> Right. >> Right, so that is a lot of people are seeing opportunity there. Where is maybe, I've had people say, you know, well you don't really have to protect data in the cloud it's all stuff that's magically protected, I've had customers say that to me and I think that we're now beyond that, right and people are realizing, wow you know, just as much of a need or more of a need than it was before. So I think there's plenty of you know companies appreciate opportunity and they see opportunity right now as data protection evolves quickly to address the new IT world that we live in. On anything you would add to the first answer? >> Yeah so I think, several years ago VMworld feels like a storage shelf you know. I think there is still a lot of exciting interesting storage company but there has been quite a bit of consolidation you know. Software defined storage it seems like that market's landscape is becoming clearer and clearer and we're definitely seeing that spreading into secondary storage is now right for a disruption and we're also seeing that is disruption around secondary storage isalso impacting data protection software. It's not just the secondary storage element but you know extent to the entire software stack. I think it's very exciting and also thinking about you know what is going to be the economical benefit of cloud and how do we take best advantage of that and this is why you know our AWS relationship. You know we are rejuvenizing our DR effort. We have successful on prem product like SRM but we're seeing tremendous new opportunity to look at that in the context of cloud to truly leveraging the economy is scale of what cloud has to offer. So lots of driving factors to really revitalize that. >> It's a cloud show and you have no cloud. >> Okay Beth second part of my question is how do you keep pace, it's a pretty tremendous innovations going on, how do you keep pace, what are your thoughts on all that? >> So the really cool thing is because where you know we're Dell Technologies we have not only data protection assets, we also have servers, we also have switches, we have everything we need to build a full integrated stack which we now have without EPA. So within a integrated data protection appliance we have the best of data domain, we have the best of our software, we're leveraging also power at servers and dellium C switches. So we have everything that we need to build that end to end best in class integrated appliance and as customers change how they consume data protection to more like a converged consumption model or hyper converged consumption model we have all the pieces that we need to make that a reality and then to continue to move forward. So when you combine that with our relationship with VMware and the ability that we have to drive innovation jointly I have no doubt that we're going to be really moving ahead into you know modern data protection. >> Final question before we rap. R&D comes up, Micheal also mention and so do Pat, billions of dollars now are in R&D. Free cash was a billion dollars. Three billion for VMware. A lot of observations this week that we kind of looked and read the tea leaves one of them was at least for me was the stack a collision between hardware software stacks as IoT and servers and devices, you have hardware stacks and software stacks. Untested scenario certainly in vSAN; You see a lot of activity around untested new use cases and so it's going to put pressure on engineers. So the question is what's the vision for the R&D for you guys around data protection, because it's not just data protection anymore it's a fundamental linchpin in the equation of cloud >> Yeah. >> Thoughts on engineering road map I mean engineering R&D. >> One thing we're doing actually right now this week is we're restructuring our EMC lab dellium c lab back in Hopkinton to move to more of an open shared pivotal type environment. So you know it's clear that as we go forward doing things like pere programming on test driven development. You know enabling continuous always good known stayed like there is definitely advancements happening in software development that are accelerating innovation and so as we take advantage of that, that's how we keep pace with what's going on around us. Because you're right the number of things to get involved in is endless. >> I just want to point out before we end the segment you guys are very inspirational women in tech. I think you guys are amazing. We talk about the engineer resources. >> Thank you John. Your thoughts on the industry, as there's a lot of controversy in Silicon Valley and around the world around STEM and women in tech. Thoughts that you'd like to share to all the men watching and all the folks and young girls who might inspiration. You know it's passionate for us. >> Yeah, I'll start. So I think, first of all I want to tank the Cube for having such awareness in this topic and you know constantly featuring women in tech on your shows. You guys have been doing a great job raising the visibility women leaders. >> Thank you >> Thanks >> in the industry. Thank you. So certainly this is a topic very dear and near to my heart. This week you know we can still see not only our employee base but our customer base is heavily men dominated. But I think we're seeing unprecedented levels of awareness and attention to this topic in Silicon Valley and across the world. Really I do think we are starting to see much better transparency metric. We're seeing increased accountability in business and business leadership. So I think those and we're seeing a lot of social awareness I think those are going to drive a positive change. So let me give you a concrete example of fuzz for example things we do in VMware, we just gone through bonus allocation and compensation adjustment. I would get a report from it make sure, comparing the percentage of what we have done for the men population and women population and so you get a real time feedback in data and when we see the data is actually quite shocking hopefully we do see, unconsciously you know we may be allocating those >> Unconscious bias if you will. >> Yeah those differently. But because of those real time data and feedback we're good able to you know keep ourself accountable. So just you know this is no longer just talk this is a real data you know in the real HR practices that we are already building into our day to day practice. So I think I'm very optimistic, this will take time but this is you know we're moving in the right direction. >> Historical moment in the world if you think about it. This is super important time. The inspiration and also the young women out there too and also for the men. They need to be aware as well because inclusion includes not just women it's everyone. That seems to be >> Absolutely. >> In fact a trend we had an interview on the Cube and our Simpson who works for Mozilla she's doing some work for Tech Nation, she said they're changing it from diversity inclusion to inclusion and diversity. They're flipping it around where inclusion leads diversity cause they want to lead with the message of inclusion; >> Yeah. >> as a primary message with diversity. So it's not just the diversity message it's inclusion. >> Yeah. >> Love that. >> Yeah the only thing I would add would be the phrase "She can be it if she sees it" I think having people like myself and Yanbing be visible role models it's very impactful, especially for young women to see you know women in tech leadership positions. It's hard to imagine yourself in a role if you don't see anyone similar to in a role. So I think the more that people like us and our peers get out there and really put an effort into being visible. >> Do you see the networks forming more, I mean is there more action flowing happen. Can you compare and contrast just even a few years ago is it on the rise significantly? >> I think it's on the rise. >> Yeah I do get us to be involved in a lot of opportunistic situations, yeah. >> And of course your Twitter handle puts it right out there, @ybhighheels. >> Yeah. >> Right, your not shy about it. >> Yeah, there's nothing shy about it. I realize you know Beth and I, we are both addressed in very feminine way. I do think. >> Your capabilities are off to chart you to great and impressive executives. >> Society is increasingly more inclusive about their notions of female tech leader. It's not just one size fits all and I think it's encouraging us to show who we really are and the authentic self and I think that's very important for young girls to see because I remember when I was a young girl I didn't go into tech expecting I do not get to be who I am >> Yeah and that shouldn't reflect your capability of anyway any kind and that seem to be the greater awareness. The Google memo that went around as all of it so getting us some great videos on Silicon Angle on that topic. Again you guys are great inspiration. We love working with you you guys are great executives. >> Thank you. >> Its great content. >> Your welcome. >> We super passionate about it. We'll be at Grace Hopper for our 4th year we do that. >> Fantastic. >> As we show every year, we're learning more and more and we're going to do a podcast for guys too. >> Nice. >> Different angle. >> Love that. >> A lot of guys want to do what to do. >> Okay that's great. >> Inclusion and diversity of course; I need the help. I'm John Furrier With Dave Vellante Here. Live at Vmworld. More coverage coming after this short break.

>> Announcer: Live from San Francisco, it's The Cube covering Informatica World 2017. Brought to you by Informatica. >> Okay, welcome back everyone. We are here live in San Francisco for The Cube's exclusive coverage of Informatica World 2017. I'm John Furrier, this is Siliconangle's flagship program, we go out to the events and (he mumbles). My next guest is Jitesh Ghai who's the Vice President General Manager of data quality and governance for Informatica. Welcome to The Cube, thanks for joining us today. >> Happy to be here, John. Pleasure. >> So, two things right out of the gate. One, data quality and governance, two of the hottest topics in the industry, never mind within Informatica. You guys are announcing a lot of stuff, customers are pretty happy, you got a solid customer base. >> That's right. >> Product's been blooming, you got a big brand behind you now. This is important. There's laws now in place coming online in 2018, I think it's the GDPR. >> That's right. >> And there's a variety of other things, but more importantly customers got to get hold of their data. >> That's right. >> What's your take and what are you announcing here at the show? >> Well, you know, from a data governance and compliance and overall quality standpoint, data governance started off as a stick, a threat of regulatory pressure, but really the heart of what it is is effective access to and consumption of data, trusted data. And through that exercise of the threat of a stick, healthy practices have been implemented and that's resulted in an appreciation for data governance as a carrot, as an opportunity to innovate, innovate with your data to develop new business models. The challenge is as this maturation in the practice of data governance has happened there's been a realization that there's a lot of manual work, there's a lot of collaboration that's required across a cross-functional matrixed organization of stakeholders. And there's the concept of ... >> There's some dogma too, let's just face it, within organizations. I got all this data, I did it this way before. >> Right. >> And now, whoa, the pressure's on to make data work, right, I mean that's the big thing. >> That's exactly right. So, you collaborate, you align, and you agree on what data matters and how you govern it. But then you ultimately have to stop documenting your policies but actually make it real, implement it, and that's where the underlying data management stack comes into place. That could be making it real for regulatory, financial regulations, like BCBS 239 and CCAR, where data quality is essential. It could be making it real for security related regulations where protection is essential, like GDPR, the data protection regulation in the EU. And that's where, Informatica is launching a holistic enterprise data governance offering that enables you to not just document it, or as one CDO said to me, "You know, at some point you've got to stop talking about it, "you actually have to do it." To connecting the conceptual, the policies, with the underlying physical systems, which is where intelligent automation with the underlying data management portfolio, the industry-leading data management portfolio that we have, really delivers significant productivity benefits, it's really redefining the practice of data governance. >> Yeah, most people think of data as being one of those things, it's been kind of like, whether it's healthcare, HIPAA old models, it's always been an excuse to say no. "Whoa, we don't do it that way." Or, "Hey." It's kind of become a no-op kind of thing where, "No, we don't want to do any more than data." But you guys introduced CLAIR which is the acronym for the clairvoyant or AI, it's kind of a clever way to brand. >> That's right. >> That's going to bring in machine learning augmented intelligence and cool things. That only, to me, feels like you're speeding things up. >> That's exactly right. >> When in reality governance is more of a slowdown, so how do you blend the innovation strategy of making data freely available ... >> Right. >> ..and yet managing the control layer of governance, because governance wants to go slow, CLAIR wants to go fast, you know. Help me explain that. >> Well, in short, sometimes you have to go slow to go fast. And that's the heart of what our automated intelligence that CLAIR provides in the practice of data governance, is to ensure that people are getting access to, efficient access to trusted data and consuming it in the right context. And that's where you can set, you can define a set of policies, but ultimately you need those policies to connect to the right data assets within the enterprise. And to do that you need to be able to scan an entire enterprise's data sets to understand where all the data is and understand what that data is. >> Talk about the silver bullet that everyone just wants to buy, the answer to the test, which is ungettable, by the way, I believe, we just had Allegis on, one of your customers, and their differentiation to their competition is that they're using data as an asset but they're not going all algorithmic. There's the human data relationship. >> Absolutely. >> So there's really no silver bullet in data. You could use algorithms like machine learning to speed things up and work on things that are repeatal tasks. >> Right. >> Talk about that dynamic because governance can be accelerated with machine learning, I would imagine, right? >> Absolutely, absolutely. Governance is a practice of ensuring an understanding across people, processes and systems. And to do that you need to collaborate and define who are the people, what are your processes, and what are the systems that are most critical to you. Once you've defined that it's, well, how do we connect that to the underlying data assets that matter, and that's where machine learning really helps. Machine learning tells you that if you define customer id as a critical data element, through machine learning, through CLAIR, we are able to surface up everywhere in your organization where customer id resides. It could be cmd id, it could be customer_id, could be customer space id, cust id. Those are all the inferences we can make, the relationships we can make, and surface all of that up so that people have a clear understanding of where all these data assets reside. >> Jitesh, let's take a step back. I want to get your thoughts on this, I really want you to take a minute to explain something for the folks watching. So, there's a couple of different use cases, at least I've observed in a row and the wikibon team has certainly observed. Some people have an older definition of governance. >> Right. >> What's the current definition from your standpoint? What should people know about governance today that's different than just last year or even a few years ago, what's the new picture, what's the new narrative for governance and the impact to business? >> You know, it's a great question. I held a CDO summit in February, we had about 20 Chief Data Officers in New York and I just held an informal survey. "Who implements data governance programs "for regulatory reasons?" Everybody put their hand up. >> Yeah. >> And then I followed that up with, "Who implements data governance programs "to positively affect the top line?" and everybody put their hand up. That's the big transition that's happened in the industry is a realization that data governance is not just about compliance, it's also about effective policies to better understand your data, work with your data, and innovate with your data. Develop new business models, support your business in developing those new business models so that you can positively affect the top line. >> Another question we get up on The Cube all the time, and we also observe, and we've heard this here from other folks at Informatica and your customers have said, getting to know what you actually have is the first step. >> Right. >> Which sounds counter-intuitive but the reality is that a lot of folks realize there's an asset opportunity, they raise their, hey, top line revenue. I mean, who's not going to raise their hand on that one, right, you get fired. I mean, the reality is this train's coming down the tracks pretty fast, data as an input into value creation. >> That's exactly right. >> So now the first step is oh boy, just signed up for that, raise my hand, now what the hell do I have? >> Right. >> How do you react to that? What's your perspective on that? >> That's where you need to be able to, google indexed the internet to make it more consumable. Actually, a few search engines indexed the internet. Google came up with sophistication through its page-ranking algorithm. Similarly, we are cataloging the enterprise and through CLAIR we're making it so that the right relevant information is surfaced to the right practitioner. >> And that's the key. >> That is the key. >> Accelerating the access method, so increase the surface area of data, have the control catalog for the enterprise. >> That's right. >> Which is like your google search analogy. A little harder than searching the internet, but even google's not doing a great job these days, in my opinion, I should say that. But there's so many new data points coming in. >> That's right. >> So now the followup question is, okay, it's really hard when you start having IOT come in. >> That's right. >> Or gesture data or any kind of data coming in. How do you guys deal with that? How does that rock your world, as they say? >> And that's where effective consumption of data permeates across big data, cloud, as well as streaming data. We have implemented, in service to governance, we've implemented in-stream data quality rules to filter out the noise from the signal in sensor data coming in from aircraft subsystems, as an example. That's a means of, well, first you need to understand what are the events that matter, and that's a policy definition exercise which is a governance exercise. And then there's the implementation of filtering events in realtime so that you're only getting the signal and avoiding the noise, that's another IOT example. >> What's your big, take your Informatica hat off, put your kind of industry citizen hat on. >> Mm-hm. >> What's your view of the marketplace right now? What's the big wave that people are riding? Obviously, data, you could say data, don't say data 'cause we know that already. >> Sure. >> What should people, what do you observe out there in the marketplace that's different, that's changing very rapidly? Obviously we see Amazon stock going up like a hockey stick, obviously cloud is there. What are you getting excited about these days? >> You know, what I'm excited about is bringing broad-based access of data to the right users in the right context, and why that's exciting is because there's an appreciation that it's not the analytics that are important, it's the data that fuels those analytics that's important. 'Cause if you're not delivering trusted, accurate data it's effectively a garbage in, garbage out analytics problem. >> Hence the argument, data or algorithms, which one's more important? >> Right. >> I mean data is more important than algorithms 'cause algorithms need data. >> That's exactly right and that's even more true when you get into non-deterministic algorithms and when you get into machine learning. Your machine learning algorithm is only as good as the data you train it with. >> I mean look, machine learning is not a new thing. Unsupervised machine learning's getting better. >> Right. >> But that's really where the compute comes in, and the more data you have the more modeling you can do. These are new areas that are kind of coming online, so the question is, to you, what new exciting areas are energizing some of these old paradigms? We hear neural nets, I mean, google's just announced neural nets that teach neural nets to make machine learning easier for humans. >> Right. >> Okay. I mean, it has a little bit of computer science baseball but you're seeing machine learning now hitting mainstream. >> Right. >> What's the driver for all this? >> The driver for all this comes down to productivity and automation. It's productivity and automation in autonomous vehicles, it's productivity and automation that's now coming into smart homes, it's productivity and automation that is being introduced through data-driven transformation in the enterprise as well, right, that's the driver. >> It's so funny, one of my undergraduate computer science degrees was databases. And in the '80s it wasn't like you went out to the tub, "Hey, I'm a databaser." (He mimics uncertain mumbling) And now it's like the hottest thing, being a data guy. >> Right. >> And what's also interesting is a lot of the computer science programs have been energized by this whole software defined with cloud data because now they have unlimited, potentially, compute power. >> Right. >> What's your view on the young generation coming in as you look to hire and you look to interview people? What are some of the disciplines that are coming out of the universities and the masters programs that are different than it was even five years ago? What are some trends you're seeing in the young kids coming in, what are they gravitating towards? >> Well, you know, there's always an appreciation of, a greater appreciation for, you know, the phrase I love is, "In god we trust, all others must have data." There's an increasing growing culture around being data-driven. But from a background of young people, it's from a variety of backgrounds, of course computer science but philosophy majors, arts majors in general, all in service to the larger cause of making information more accessible, democratizing data, making it more consumable. >> I think AI, I agree, by the way, I would just add, I think AI, although it is hyped and I don't really want to burst that bubble because it's really promoting software. >> Right. >> I mean, AI's giving people a mental model of, "Oh my god, some pretty amazing things are happening." >> Sure. >> I mean, autonomous vehicles is what most people point to and say, "Hey, wow, that's pretty cool." A Tesla's much different than a classic car. I mean, you test-drive a TESLA you go, "Why am I buying BMW, Audi, Mercedes?" >> Right, exactly. >> It's a no brainer. >> Right. >> Except it's like (he mumbles), you got to get it installed. But, again, that's going to change pretty quickly. >> At this point it's becoming a table sticks exercise. If you're not innovating, if you're not applying intelligence and AI, you're not doing it right. >> Right, final question. What's your advice to your customers who are in the trenches, they raise their hand, they're committed to the mandate, they're going down the digital business transformation route, they recognize that data's the center of the value proposition, and they have to rethink and reimagine their businesses. >> Right. >> What advice do you give them in respect to how to think architecturally about data? >> Well, you know, it all starts with your data-driven transformations are only as good as the data that you're driving your transformations with. So, ensure that that's trusted data. Ensure that that's data you agree as an organization upon, not as a functional group, right. The definition of a customer in support is different from the definition of a customer in sales versus marketing. It's incredibly important to have a shared understanding, an alignment on what you are defining and what you're reporting against, because that's how you're running your business. >> So, the old schema concept, the old database world, know your types. >> Right. >> But then you got the unstructured data coming in as well, that's a tsunami IOT coming in. >> Sure, sure. >> That's going to be undefined, right? >> And the goal and the power of AI is to infer and extract metadata and meaning from this whole landscape of semi-structured and unstructured data. >> So you're of the opinion, I'm sure you're biased with being Informatica, but I'm just saying, I'm sure you're in favor of collect everything and connect the dots as you see fit. >> Well ... >> Or is that ...? >> It's a nuance, you can't collect everything but you can collect the metadata of everything. >> Metadata's important. >> Data that describes the data is what makes this achievable and doable, practically implementable. >> Jitesh Ghai here sharing the metadata, we're getting all the metadata from the industry, sharing it with you here on The Cube. I'm John Furrier here live at Informatica World 2017, exclusive Cube coverage, this is our third year. Go to siliconangle.com, check us out there, and also wikibon.com for our great research. Youtube.com/siliconangle for all the videos. More live coverage here at Informatica World in San Francisco after this short break, stay with us.

>> Commentator: Live from Las Vegas. It's theCUBE. Covering InterConnect 2017. Brought to you by IBM. >> Hello everyone. Welcome to theCUBE special broadcast here at the Mandalay Bay in Las Vegas for IBM InterConnect 2017. This is IBM's big Cloud show. I'm John Furrier. My co-host, David Vellante for the next three days will be wall-to-wall coverage of IBM's Cloud Watson. All the goodness from IBM. The keynote server finishing up now but this morning was the kickoff of what seems to be IBM's Cloud strategy here with Dave Vellante. Dave, you're listed in the keynote, we are hearing the presentation. We had the General Manager/Vice President of Data from Twitter on there, Chris Moody, talkin' about everything from the Trump presidential election being the avid tweeter that he is and got a lot of laughs on that. To the SVP of Cloud talking about DevOps and this is really IBM is investing 10 million dollars plus into more developer stuff in the field. This is IBM just continuing to pound the ball down the field on cloud. Your take? >> Well IBM's fundamental business premise is that cognitive, which includes analytics, John plus cloud plus specific industry solutions are the best way to solve business problems and IBM's trying to differentiate from the other cloud guys who David Kenny was on stage today saying, you know, they started with a retail business or the other guys started with search, we started with business problems, we started with data. And that's fundamental to what IBM is doing. The other point, I think is-- the other premise that IBM is putting forth is that the AI debate is over. The Artificial Intelligence, you know, wave of excitement in the 70s and 80s and then, you know, nothing is now back in full swing. An AI on the Cloud is a key differentiator from IBM. In typical IBM fashion for the last several Big Shows, IBM brought out not an IBMer but a customer or and or a partner. And today it brought out Chris Moody from Twitter talking about their relationship with IBM but more specifically the fact that Twitter's 11 years old. Some of the things you're doing with Twitter obviously connected into March Madness and then Arvind Krishna who has taken over for Robert LeBlanc as the head of the Cloud group, talked about IBM, AI, IBM's Cloud, blocked chain, trusted transactions, IoT, DevOPs, all the buzz words merged into IBM's Cloud Strategy. And of course, we reported several years ago at this event about Bluemix as the underpinning of IBM's developer strategy. And as well it showcased several partners. Indiegogo was a crowdfunding site and others. Some of those guys are going to be in theCUBE. So. You know as they say, this AI debate is over. It's real and IBM's intent is to the platform for business. >> Dave, the thing I want to get your thoughts on is IBM's on a 19 consecutive quarters of revenue problems with the business on general but they've been on a steady course and they kind of haven't wavered. So it's as if they know they got to shrink to grow approach but we just came off the heels of Google Next which is their Cloud Show. How the Amazon is on re-invent as the large public cloud but the number one question on the table that's going to power IoT, that's going to power AI, is the collision between cloud computing and IoT, cloud computing in big data I should say is colliding with IoT at the center which is going to fuel AI and so, it brings up the question of enterprise readiness. Okay? So this is the number one conversation in the hallways here at Las Vegas and every single Cloud Show in the enterprise is, can I move to the cloud? Obviously it's a hybrid world, multi-cloud world. IBM's cloud play. They had a Cloud. They're in the top four as we put them in there. Has to be enterprise ready but yet it as to spawn the development side. So again, your take on enterprise readiness and then really fueling the IoT because IoT is a real conversation at an architectural level that is shifting the-- tipping the scales if you will for where the action will be. >> Well John, you and I have talked in theCUBE for years now. Going on probably five years that IBM had to shrink to grow. They've got the shrink part down. They've divested some of its business like the x86 business and the microelectronics business. They have not solved the grow problem. Let's just say 19 straight quarters of declining revenue. But here's the question. Is IBM stronger today than it was a year ago? And I would argue yes and why is that? One is its focus. Its got a much clearer focus on its strategy around cognitive, around data and marrying that to Cloud. I think the other is as an 80 billion dollar company even though it's shrinking, its free cash flow is still 11.6 billion. So it's throwing off a lot of cash. Now of course, IBM made those numbers, made its earnings numbers by with through expense control, its got lower tax right. Some of the new ones of the financial engineering. Its got some good IP revenue. But nonetheless, I would still argue that IBM is stronger this year than it was a year ago. Having said that, IBM's service as business is still 60% of the company. The software business is still only about 30% into it but 10% is hardware. So IBM-- people say IBM has exited the hardware business. It hasn't exited completely the hardware business but it's only focusing on those high value areas like mainframe and they're trying to sort of retool power. Its got a new leader with Bob Picciano but it's still 60% of the company's business is still services and it's shifting to a (mumbles) model. An (mumbles) model. And that is sometimes painful financially. But again John, I would argue that it is stronger. It is better positioned. And now its got some growth potential in place with AI and with, as you say, IoT. We're going to have Harriet Green on. We're going to have Deon Newman on. Focusing on the IoT opportunity. The weather company acquisition as a foundation for IoT. So the key for IBM is that it's strategic imperatives are now over 40% of its business. IBM promised that it would be a 40 billion dollar business by 2018 and it's on track to do that. I think the question John is, is that business as profitable as its old business? And can it begin growing to offset the decline in things like storage, which has been seeing double digit declines and its traditional hardware business. >> So Dave, this is to my take on IBM. IBM has been retooling for multiple years. At least a five year journey that they have to do because let's just go down the enterprise cloud readiness matrix that I'm putting together and let's just go through the components and then think about what was old IBM and what's new. Global infrastructure. Compute networking, storage and content delivery, databases, developer tools, security and identity, management tools, analytics, artificial intelligence, Internet of Things, mobile services, enterprise applications, support, hybrid integration, migration, governance and security. Not necessarily in that order. That is IBM, right? So this is a company that has essentially (mumbles) together core competencies across the company and to me, this is the story that no one's talking about at IBM is that it's really hard to take those components and decouple them in a fashion that's cloud enabled. This is where, I think, you're going to start to see the bloom on the rose come out of IBM and this is what I'm looking at because IBM had a little bit here, they had a little bit here, then a little stove pipe over here. Now bringing that together and make it scalable, it's elastic infrastructure. It's going to be really the key to success. >> Well, I think, if again if you breakdown those businesses into growth businesses, the analytics business is almost 20 billion. The cloud business is about 14 billion. Now what IBM does is that they talk about as a service runway of you know, 78 billion so they give you a little dimensions on you know, their financials but that cloud business is growing at 35% a year. The as a service component, let's call it true cloud, is growing over 60% a year. Mobile growing, 35%. Security, 14%. Social, surprisingly is down actually year on year. You would thought that would be a growth theory for them but nonetheless, this strategic initiatives, this goal of being 40 billion by 2018 is fundamental to IBM's future. >> Yeah and the thing too about the enterprise rate is in the numbers, it speaks to them where the action is. So right now the hottest conversations in IT are SLA's. I need SLA's. I have a database strategy that has to be multi-database. So (mumbles) too. Database is a service. This is going to be very very important. They're going to have to come in and support multiple databases and identity and role-based stuff has to happen because now apps, if you go DevOps and you go Watson Data Analytics, you're going to have native data within the stack. So to me, I think, one of the things that IBM can bring to the table is around the enterprise knowledge. The SLA's are actually more important than price and we heard that at Google Next where Google tried it out on their technologies and so, look at all the technology, buy us 'cause we're Google. Not really. It's not so much the price. It's the SLA and where Google is lacking as an example is their SLA's. Amazon has really been suring up the SLA's on the enterprise side but IBM's been here. This is their business. So to me, I think that's going to be something I'm going to look for. As well as the customer testimonials, looking at who's got the hybrid and where the developer actually is. 'Cause I think IoT is the tell sign in the cloud game and I think a lot of people are talking about infrastructures of service but the actual B-platform as a service and the developer action. And to me, that's where I'm looking. >> Well comparing and contrasting, you know, those two companies. Google and Amazon with IBM, I think completely different animals. As you say, you know, Google kind of geeky doesn't really have the enterprise readiness yet although they're trying to talk that game. Diane Green hiring a lot of new people. AWS arguebly has, you know, a bigger lead on the enterprise readiness. Not necessarily relative to IBM but relative to where they were five years ago. But AWS doesn't have the software business that IBM has yet. We'll see. Okay so that's IBM's ace in the hole is the software business. Now having said that, David Kenny got on stage today. So he came out and he's doing his best Jeremy Burton impression. Came out in sort of a James Bond, you know, motif and guys with sunglasses and he announced the IBM Cloud Object Storage Flex. And he said, yes we have a marketing department and they came up with that name. You know, this to me is their clever safe objects tour to compete with S3, you know several years late. After Amazon has announced S3. So they're still showing up some of that core infrastructure but IBM's-- the (mumbles) of IBM strategy is the ability to layer cognitive and their SAS Portfolio on top of Cloud and superglue those things together. Along, of course, with its analytics packages. That's where IBM gets the margin. Not in volume infrastructure as a service. >> I want to get your take on squinting through the marketing messages of IBM and get down to the meat and the bone which is where is the hybrid cloud? Because if you look at what's going on in the cloud, we hear the new terms, lift and shift. Which to me is rip and replace. That's one strategy that Google has to take is if you run (mumbles) and Google, you're kind of cloud native. But IBM is dealing a lot at pre-existing enterprise legacy stuff. Data center and whatnot so the lift and shift is an interesting strategy so the question is, for you is, what does it take for them to be successful? With the data platform, with Watson, with IoT, as enterprise extend from the data center with hybrid. >> Well I think that, you know, again IBM's (mumbles) is the data and the cognitive platform. And what IBM is messaging to your question is that you own your data. We are not going to basically take your data and form our models and then resell your IP. That's what IBM's telling people. Now why don't we dig into that a little bit? 'Cause I don't understand sort of how you separate the data from the models but David Kenny on stage today was explicit. That the other guys, he didn't mention Google and Amazon, but that's who he was talkin' about, are essentially going to be taking your data into their cloud and then informing their models and then essentially training those models and seeping your IP out to your competitors. Now he didn't say that as explicitly as I just did but that's something as a customer that you have to be really careful of. Yes, it's your data. But if data trains the models, who owns the model? You own the data but who owns the model? And how do you protect your IP and keep it out of the hands of the competitors? And IBM is messaging that they are going to help you with the compliance and the governance and the (mumbles) of your organization to protect your IP. That's a big differentiator if in fact there's meat in the bone there. >> Well you mentioned data, that's a key thing. I think whether doing it really quickly is getting the hybrid equation nailed so I think that's going to like just pedal as fast as you can. Get that going. But data first enterprise is really speaks to the IoT opportunity and also the new application developers. So to me, I think, for IBM to be successful, they have to continue to nail this data as value concept. If they can do that, they're going to drive (mumbles) and I think that's their differentiation. You look at, you know, Oracle, Azure, Microsoft Azure and IBM, they're all playing their cards to highlight their differentiation. So. Table stakes infrastructures of service, get some platform as a service, cloud native, open source, all the goodness involved in all the microservices, the containers, Cooper Netties, You're seeing that marker just develop as it's developing. But for IBM to get out front, they have to have a data layer, they have to have a data first strategy and if they do that well, that's going to be consistent with what I think (mumbles). And so, you know, to me I'm going to be poking at that. I'm going to be asking all the guests. What do you think of the data strategy? That's going to be powering the AI, you're seeing artificial intelligence, and things like autonomous vehicles. You're seeing sensors, wearables. Edge of the network is being redefined so I'm going to ask the quests really kind of how that plays out in hybrid? What's your analysis going to be for the guests this week? >> Well, I think the other thing too is the degree to-- to me, a key for IBM success and their ability to grow and dominate in this new world is the degree to which they can take their deep industry expertise in health care, in financial services and certain government sectors and utilities, et cetera. Which comes from their business process, you know, the BPO organization and they're consulting and the PWC acquisition years ago. The extent to which they can take that codifier, put it in the software, marry it with their data analytics and cognitive platforms and then grow that at scale. That would be a huge differentiator for IBM and give them a really massive advantage from a business model standpoint but as I said, 60% of the IBM's business remains services so we got a ways to go. >> Alright. We're going to be drilling into it again. There's a collision between cloud and big data markets coming together that's forming the IoT. You can see machine learning. You can see artificial intelligence. And I'm really a forcing function in cloud acceleration with data analytics being the key thing. This is theCUBE. We'll be getting the data for you for the next three days. I'm John Furrier. With Dave Vellante. We'll be back with more coverage. Kicking off day one of IBM InterConnect 2017 after the short break.