from the silicon angle media office in Boston Massachusetts it's the queue now here's your host David on tape hello everyone and welcome to this special cube conversation you know Hewlett Packard Enterprise has gone through one of the most significant transformations in the history of the tech business once a much larger in far-flung conglomerate HP as you know split in two and now HPE is much more focused and has a completely different posture with respect to technology partners so today we're gonna focus in on the big drivers of innovation in the technology business data AI and cloud and get HPE spointer222 digging to two areas of growth hyper-converged infrastructure and intelligence storage I also want to share some ETR data using simply and nimble as proxies for these markets finally we want to peek into some of the spending data in HPE zico system to see how a more partner friendly HPE is faring and with me today is Patrick Osbourne Patrick is the vice president and GM of big data analytics and scale-out data platforms at Hewlett Packard Enterprise and a friend of the cube Patrick always a pleasure thanks for coming in thanks so much for having him so let me set it up here and I want to share some spending data with our audience Alex if you bring up the the first slide I want to show is this shows the the latest spending data just released from ETR on the various segments and you and it's a double y-axis and you can see in the left hand side is the average spend represented by the size of the charts on the right hand side is the growth rate represented by the dots and I've highlighted in green some of the key areas that we're going to talk about analytics bi big data you can see 12% still pretty big market even ten years into the big data theme cloud computing you know growing 15 16 % ml AI 17% you can see the container space is growing it between 15 and and 20 percent so Patrick let's start with what's in your title the big data you know the analytics piece you know what are you seeing there what's HP story yes so that's been a area growth for us within HPE not only from an infrastructure but also a services play we've got a number of you know big partners in the traditional you know big data space we made a number of you know strategic acquisitions over the last two years in this area specifically around blue data nap are so these areas that customers are in you know continue to invest in in the macro area are very important and well I think one of the things you're seeing here from a growth perspective is that they're also bringing in some very adjacent markets with AI and ml so it's part of an entire workflow so you start off with bi analytics big data and we have a number of solutions around that area and then starting to add in things like AI AM LDL into that analytics work workflow so it's been really good for us you're really kind of adding into your portfolio they're like say the map bar acquisition they they kind of were one of the the big three that started that whole big data movement and then now you have this organizations with these troves of data and they're trying to figure out okay what do we do with it and that's really where machine intelligence or AI comes in isn't it absolutely and not only you know we're we providing a number of solutions for customers in this area but we're using it ourselves to write to you know enhance our customer experience enhance our automation support automation I definitely give a you know much better customer experience with our storage and data platforms so wait you send your practitioners of AI to make your customers lives better by M you're saying by embedding that into storage platform you know if you take a look at a number of our marquee services that we have whether it's things like info site Green Lake even a rubra central you know think about some of the things that we do at the edge all that is being powered by AI right at the end of the day so we're using those techniques to improve the product and solution experiences for you know a number of our products everything from it started with nimble we added 3par now we've got simplicity in the info site and as we start to bring together some of the workloads at the edge right with Aruba and things we're doing there it's you know the customers are obviously voting with their dollars all right let's talk about cloud generally but specifically I want to get into hybrid containers McLeod has permanently changed you know our industry everybody wants to bring that cloud model on Prem it's clearly a hybrid world you could see containers really growing Stu Minutemen has a premise that look containers and kubernetes that we treat them as a separate thing but it's really being embedded into all parts of the portfolio so what's your point of view on on containers hybrid bring us up to speed on what HPE is doing there yeah so that's definitely fueling a lot of our growth not only in what you think about the traditional storage segments but as well as HCI right so you know when we talk later about some of the growth we're seeing nimble and simplicity we've got a number of solutions that sit you know directly within this container container orchestration container management we've got you know things that we develop on our own we made a huge announcement at kuba con right around the HPE container platform so for customers that want to run these analytics AI ml very data oriented applications that run in containers we have a great platform for that an HP container platform we could run that on bare metal we can run that in simplicity for example so we're seeing a lot of fuel for that not only just servicing some of the storage and data needs for containers right but also being able to provide an info site like experience for this new generation of application development and were close how do you see the edge fitting into this you know we interviewed Antonio recently with John Chambers at the pensando announcement and and that was kind of interesting you see do you see that as a as a pendulum swing or sort of an expansion of the cloud if you will yeah I definitely see it as an expansion when we talk at HPE we want to be an edge to quarter cloud you know company and helping customers navigate the digital transformation in hybrid IT right and then we're gonna offer that to customers as a service through Green Lake we've been pretty public about that and so one of the big opportunities we see is around these distributed data centers some people define a distributed edge whether that's customers who are doing autonomous vehicles autonomous drilling we see a number of you know big-box retailers you know for example that don't necessarily have a traditional data center but it's not so far out into the edge that it's like an autonomous vehicle but they have you know the similar concerns in terms of a distributed nature how do you automate that how do you manage that at scale and so these assets that we bring together with things like Aruba and our edge line servers and managing that data experience is something that we're gonna capitalize on in FY 24-hour constant retail is interesting right every Nevitt has a Amazon war room but many sectors as a retailer really on fire right now people trying to take advantage of their their store presence yep when IOT is a big factor there so you're seeing a lot of that action is HP yeah absolutely and those customer those customers of ours are fueling their growth through digital transformation so they're using containers and kubernetes and this new style of application development and they want to be able to distribute those data centers and that data but they also have to make it simple right so you see the march towards what we you know are platforms like simplicity for HCI some of the offerings we have around you know independently scalable three-tiered architectures but you get the best of HCI with that we call it nimble d HCI all right so we have a number offerings for customers who you know really want that scale and in serviceability alright let's let's let's pivot a little bit and talk about some of that infrastructure Alice you bring up the next slide what I want to talk to here is this is the ETR data every time they do one of these surveys they ask essentially you're spending more are you spending less and they subtract the less from the more and that's what they call net score net score remember is a measure of spending momentum now what we've done here is you can see the filtered end of 313 HPE customers out of the thousand plus survey respondents of this quarter and you can see a good mix of enterprise size and industry and it's a lot of North America but but good regional - and we're showing the net scores breakdown for for two of your platforms simplicity which is the HCI and nimble storage and you can see the bright green is people adding to the platform the sort of darker green is spending more so let's start with Pleasant levity HCI still a really hot in growing space you've got a nets or of 38 percent almost which is very very strong in ETR parlance you know it's not off the charts like some new tech but it's really really solid so what's the update on simplicity and HCI yeah so I mean this is obviously from from a market perspective HCI is a rapidly growing space still right there's a lot of room for growth both Brown field as well as green field opportunities in the core data center at the edge even in hybrid cloud format so for us it's all about new logo acquisition for simplicity we've shown a phenomenal growth rate for that technology stack developed here in Massachusetts are a great local company great story and so for us this HCI the the markets that we're playing in we take a look at storage and data management in general sub segments of the market are growing rapidly right take a look at HCI you take a look at SDS you take a look at all flash and so we have some great offerings in that space that are completely differentiated from a customer experience and a technology experience and they work together so for example simplicity we just announced earlier in the and later in the calendar year in 2019 that we would be offering simple ibbity with an info site right so you have the same experience that you get from nimble right you get with our HCI products so we're driving those experiences together obviously you know all flash is a huge growing category within storage nimble it's got some great growth they're not only just for new logo adoptions but expansion capability so we're you know - two great products that were seeing some success in yeah so let's talk about nimble the Alex could show that data again so neighborhoods got a net score of 46 percent which again a lot of momentum I mean smaller you know sample size but still really you know strong and you can see it's a more mature market so you see maybe fewer adoptions but almost 50 percent of your customers are saying they're gonna spend more this this quarter relative to last period so that's showing momentum you mentioned info site which is really the technology that sort of nimble brought to your company which are pushing out through the portfolio so your thoughts on that yes so I mean at the end of the day customers are you know the products themselves are great and they provide the customers a really good experience we're driving all that together at a meta layer right so we talked about the products and solutions for us the strategies around the intelligent data platform right so we have a number of platforms that can help dress a number of different workloads whether it's HCI disaggregated HCI whether it's all flash whether it's you know container workloads and container orchestration but we want to provide a very good experience that you can consume as a service and we're driving that together across product lines with data services that work both on Prem and in the cloud right so we have HPE cloud volumes and a number of our Cloud Data Services that tie these platforms together so for us it's all about a strategy around this intelligent data platform not just individual products the individual products are great but from a strategy perspective that is definitely resounding with customers well you talked about digital transformation earlier Patrick I think that's important it's it customers want solutions they don't want to certainly don't want to provision loans they don't want to think about managing boxes so they really want that infrastructure to be invisible they want to push their folks up the stack yep to just do more strategic things and it's it's really your Rd that they're looking toward to automate a lot of those mundane tasks isn't it yeah they look towards RI Rd as well as they look to HPE as a portfolio company to bring together a solution stack that's gonna work for them and sometimes that solution stack is comprised of some of our partners as well so we pick some of the best partners in the industry to go work with in some of these hottest you know portions of the market that are growing significantly so in the areas of HCI or in the areas of software-defined storage you know we've got a number of folks that we that we partner with hybrid cloud and we are able to bring you know a full complete solution to a customer and we D risk that for our customers at the end of the day right we've got some great partnerships with some great companies and that's really you know suited HP very well well great segue let's talk about some of those partnerships so you when when hewlett-packard split into two companies it opened up a ton of opportunities for partnerships for you guys you got a great distribution channel and what I'm showing here Alex on this next slide if you bring this up is three partners that are gaining a lot of momentum based on the spending ETR spending data in the surveys Kohi City theme and Nutanix now remember ETR uses this concept of of net score which we talked about and I'm gonna talk about that a little bit but also market share market share is a measure of pervasiveness in other words how how much there be mentioned inside of the service so I'm showing here market shares but also net scores and you can see Kohi city is just starting in the survey so starting to you know get more noticed and then you can see Veeam and Nutanix you know with the consistent long steady market share growth this is again within the hewlett-packard enterprise account based at 313 respondents so you can see there all three are doing very well and and look at the net scores for cohesive off-the-charts 74% growing very very rapidly again smaller sample size Nutanix much larger sample size you know 60% net score so very very strong in Veen you know surprisingly for a pretty mature company with a 45% net score again very very strong so talk about the the partnerships the new HPE partner posture and then we can maybe get into what you're seeing in the market with some of these partners yes so from for HPE you know we listen to our customers in terms of you know what their their challenges are part of my business is managing around scale out data platforms and so the data is always growing and so we're seeing you know this big trend of scale out architectures powered by you know ubiquitous very high bandwidth low latency networking in the data center and outside the data center and so we're able to you know put some of these software stacks on our infrastructure that works very well with our our you know our own IP solutions and you know solve a number of critical problems for customers around secondary storage right it's growing you want to make use of it to backup and disaster recovery it's always a problem it's definitely an opportunity around hybrid cloud HCI in SDS right it has many forms and flavors right and we want to be able to provide those solutions to our customers especially if you're doing hybrid or private cloud so a lot of these partners you know we want to you know provide a full stack solution to our customers and you know these have partners help us do that how are you I mean the the you've got HCI wouldna Tanic you've got HCI with simplicity you've got sort of certainly beam and cohesively compete up how do you guys position and the a let's start with the HCI piece huh you just let customers sort of direct you and guide you or you guide them how does that all work yeah I mean we always listen to the customer first but at the end of the day we you know we lead with our own IP and we have some you know we have two great solutions around the HCI framework where you going for a very simple very scalable solution in simplicity that has some very powerful data services great economics for the HCI market and you know you see the growth and sympathy for that then we have a number of other solutions specifically around nimble called DHC I write what we're finding is that customers as a classic customers that want to they want the simplicity of management that you'd get from from HCI but they also want to be able to independently scale your compute your networking and your storage and we're able to provide that with something like nimble ProLiant our networking stack and then plug that all into info sites and it works together right so at the end of the day if I having a workload that's more appropriate to work it's on simply as a platform or it's more appropriate for DHC i we can recognize that for our customers through predictive analytics we can automate the placement of that workload and then we provide customers a set of data services so those platforms work together so it really works out well okay and then in terms of well take the situation with Nutanix so that's a customer saying hey we want you guys to work together and you say great yeah problem absolutely we'll do that so that you know we have a set of recipes and and reference architectures and offerings around those that are available direct was well through the channel and is it fair to say that the Dean viii mispronounced be even though they tried a big push in the enterprise you're a part of that that push in and and of course you know cookie city's the hot new kid in the block again is it just sort of market pull that drives that or do you have yeah I mean we definitely theme has been recognized as a great solution for customers doing you know start off you know certainly focused directly on on virtualization and then you know their their strategy is moved and you know to a very adjacent market which is how do i you know tackle that virtualization and VMS and protecting my data but in a hybrid cloud in formats so they're definitely all in on cloud I think cohesive has a very scalable file system back in and it started off with backup and recovery and now is moving into some very adjacent use cases around file secondary storage what can I do around see ICD pipelines so it's kind of approaching it from different different angles you guys really kind of changing your marketing and your product marketing really focusing more on solutions yes outcomes customer outcomes bringing that cloud model to wherever your data lives whether it's on prem at the edge talking about bringing containers throughout the portfolio bring it home what are you sort of hoping for 2020 looks like what are some of those outcomes and what should we expect from from your perspective from HP yeah so I mean we at HPE are very focused on this edge to court a cloud concept hybrid IT so all of our products have you know some sort of endemic whether it's data services or a management paradigm around hybrid cloud and so we you know we we really are you'll see that within our products product releases solution releases the people that we partner with and I think the big thing that we you know pivoted it into at the end of 2019 you'll see this accelerate significantly in 2020 is around this consumption model right the cloud consumption model with Greenlake so we talked a little bit of you know certainly Green Lake from a financial perspective but awful Green Lake as a management paradigm so Green Lake central was announced at the end of the year and just the ability to be able to you know like you do in the top of cloud right but top of private cloud or top of hybrid cloud from HPE and get a really good visibility financially into you into what you're doing it's a mindset too from the top I mean Antonio is saying everything is a service right absolutely yeah so all right Patrick hey thanks for coming in and give us the update on on HPE good luck this year and great to see it yeah thank you very much you're welcome and thank you for watching everybody this is Dave a lot day for the cube we'll see you next time thanks for watching

thanks Peter we're here with Patrick Osbourne who's the vice president and general manager of big data and secondary storage with HPE Patrick help us put into context what we've been talking about Emel ops AI and HP strategy in this area yeah thanks to I you know as you as you all have reported in the past and we've worked with with with you your HP is very strong in infrastructure solutions we've had you know quite a bit of success in AI and now deep learning cookbook which we released last year and so you know we're definitely helping customers along the maturity curve for AI and ml you see that we've got a number of advisory services I think one of the big things that we could get called out from customers is that there's a skills gap in operationalizing and and putting AI and ml workloads into production as well as you know we are a thought leader and have quite a bit of research with HP Labs on memory driven computing Gen Z and being able to squit you know scale those workloads within the enterprise so those are things that we're building off of in addition to some pretty high-profile and very valuable software acquisitions first and last year first around blue data which we talked about today in the context of ml ops and then most recently map R which is a very powerful scaleable persistent data layer for analytics so for us it's AI is a is a very is a top priority for us at HPE it's part of our corporate narrative and helping customers along that maturity curve is definitely where we're focused on great so how are HP and its partners helping customers along their journey that they're going on with AI yes so I think at the end of the day HP is very focused on our customers especially from a go-to markets perspective so we are we're in the phase now where we're helping customers not just explore but to operationalize AI and ml so whether it's cookbooks and our a specific products like machine learning operations which helps you scale from you know a data scientist or a danger engineer developing an algorithm on you know laptop to be able to running that at scale in the data center so for us that journey is is very important especially around the the outcome from the technology perspective technology partner perspective we have a number of really high profile and new relationships that were building for this new ecosystem around AI and ml and DL and so folks like data iku h2o on the hardware side Intel and NVIDIA we are bringing that to our customers to provide you know a complete solution so being able to take those ISVs and run them and containerized stateful you know deployment and then be able to you know partner with all of our hardware vendors and software vendors and then for the channel we feel that this is a huge you know this is like this is a great opportunity for them to certainly move up stack in how they talk to customers about their business outcomes so I think it's part of a three prong strategy and we're really kind of focused on those key areas yeah no doubt an area that's getting attention from all sectors of the marketplace so those that are watching HPE what should we be expecting to look to see from them in the form near future yeah so I think you know from our perspective we've got a number of releases that are coming up over the next year and pretty excited about that in addition to machine learning operations I think that the world you know will continue to be moving towards containers for more than just stateless applications we're starting now with AI and ml and I think there's a big you know future for other applications whether they're cloud native or you know those applications are refactored certainly living within a world of kubernetes right is becoming more of a reality from a deployment perspective so you know for us we're very you know focused on the customer outcome I think the other area too is that that HP has been very famous for lately is around consumption based services right so we're able to bring that vetted ecosystem the containerized deployment model and platform this the your accelerators compute networking and storage and even a persistent data layer and even you know even the the cloud experience to the customer as a biz outcome in a consumption experience their Green Lake is something that you know we think is very valuable for our customers all right well thank you patrick for helping us to put all of that into context Peter I'm going to send it back to you for the wrap

/ room tone okay opening tide on Dave in five four three thanks Peter we're here with Patrick Osbourne was the vice president and general manager of big data and analytics at Hewlett Packard Enterprise Patrick thanks for coming on thanks for having us hi so we heard from Kumar let's hear from you why did HPE purchase acquire blue data so if you think about it from three angles platform people and customers right great platform built for scale addressing a number of these new workloads and big data analytics and certainly AI the people that they have are amazing right great engineering team awesome success or customer success team team of data scientists right so you know all the folks that have some really really great knowledge in this space so they're gonna be a great addition to HPE and also on the customer side great logos major fortune 500 5 is vertical healthcare pharma manufacturing so a huge opportunity for us to scale that within the HPE context ok so talk about how it fits into your strategy specifically what are you gonna do with it what are the priorities can you share some roadmap yeah so you can take a look at HPE strategy we talked about hybrid cloud and specifically edge 2 core to cloud and this common theme that runs through that is data data-driven enterprises so for us we see blue data epic platform as a way to you know help our customers quickly deploy these new mode to applications that are fueling their digital transformation so we have some great plans we're gonna certainly invest in all the functions right so we're gonna do a force multiplier on only not only on product engineering and product delivery but also go to market and customer success we're gonna come out in our business day one with some really good reference architectures with some of our partners like cloud era h2o we've got some very scalable building block architectures to marry up the blue data platform with our Apollo systems for those of you seen that in the Marc we've got our elastic platform for analytics for customers who run these workloads now you'd be able to virtualize those in containers and we'll have you know we're gonna be building out a big services practice in this area so a lot of customers often talk to us about we don't have the people to do this right so we're gonna bring those people to you as HPE through point next advisory services implementation ongoing help with customers so it's gonna be really a fantastic start apollo's you mentioned apollo i think of apollo sometimes as HPC high performance computing and we've had a lot of discussion about how that's sort of seeping in too mainstream is that what you're seeing yeah absolutely I mean we know that a lot of our customers have traditional workloads you know they're on the path to almost completely virtualizing those right but where a lot of the innovation is going on right now is in this mode two world right so your your big data and analytics pipeline is getting longer you're introducing new experiences on top of your product and that's fueling you know essentially commercial HPC and now that folks are using techniques like AI modeling inference to make those services more scalable more automated we're starting to bringing these more of these platforms these scalable architectures like Apollo so it sounds like your roadmap has a lot of integration plans with across the HP e-portfolio we certainly saw that with nimble but blue data was working with a lot of different companies its software is the plan to remain open or is this an HPE thing yeah we absolutely want to be open so we know that we have lots of customers that choose so the HP is all about hybrid cloud right and that has a couple different implications we want to talk about your choice of on-prem versus off prem so blue data has a great capability to run some of these workloads it essentially allows you to do separation of compute and storage right and then in the world of AI and analytics and we can run some of the compute on Prem we can run it off Prem as well in the public cloud but then we also have choice for customers it you know any customers private cloud so that means they want to run on other infrastructure besides HPE we're gonna support that we have existing customers that do that we're also gonna provide infrastructure that marries the software and the hardware together with frameworks like info site that we feel will be a you know much better experience for the customers but what actually will absolutely be open and absolutely have choice all right what about business impact take the customer perspective what can they expect so I think from a customer perspective we're really just looking to accelerate deployment of AI in the enterprise right and and that has a lot of implications for us we're gonna have very scalable infrastructure for them we're gonna be really focused on this very dynamic AI and ml you know application ecosystems through partnerships and support within the blue data platform we want to provide a SAS experience right so whether that's GPUs or accelerators as a service analytics as a service we really want to fuel innovation at us as a service we want to empower those data scientists there those are they're really hard to find you know they're really hard to retain within your organization's we want to unlock all that capability and really just we want to focus on innovation of the customers yeah and they spend a lot of time wrangling data so you're really gonna simplify that with a cloud SACEM Oh Patrick thank you appreciate it thank you very much all right Peter back to you in Palo Alto clear

all right we opening tight on Dave in five four three we're back with Patrick Osbourne right Patrick let's let's wrap up here and summarize we heard how you gonna help data science teams right yep speed agility time to value all right and and I know a bunch of folks at blue data the tech is the engineering team is very very strong so you picked up a good asset there yeah it means amazing technology the founders have a long lineage of software development and an adoption in the market so we're just gonna we're gonna invested them and let them loose and then we heard they're sort of better together story from you you got a you got a roadmap you're making some investments here yeah I mean so for really focused on hybrid cloud and we want to have all these as a services experience whether it's through Green Lake or providing innovation AI GPUs as a service is something that we're gonna be you know continuing to provide our customers as we move along okay and then we heard the data science angle and the data science community and the partner angle that's that's exciting yeah I mean it's it's I think it's two approaches as well to we have data scientists right so we're gonna bring that capability to bear whether it's through the product experience or through our professional services organization and then number two you know this is a very dynamic ecosystem from an application standpoint there's commercial applications there's certainly open source and we're gonna bring a fully vetted full stack experience for our customers that they can feel confident in this you know it's a very dynamic space excel well thank you very much thank you all right now it's your turn go into the crowd chat and start talking ask questions we're gonna have polls we've got experts in there so let's crouch at clear too

(orchestral music) >> Hi, I'm Peter Burris and welcome to another digital community event. As we do with all digital community events, we're gonna start off by having a series of conversations with real thought leaders about a topic that's pressing to today's enterprises as they try to achieve new classes of business outcomes with technology. At the end of that series of conversations, we're gonna go into a crowd chat and give you an opportunity to voice your opinions and ask your questions. So stay with us throughout. So, what are we going to be talking about today? We're going to be talking about the challenge that businesses face as they try to apply AI, ML, and new classes of analytics to their very challenging, very difficult, but nonetheless very value-producing outcomes associated with data. The challenge that all these businesses have is that often, you spend too much time in the infrastructure and not enough time solving the problem. And so what's required is new classes of technology and new classes of partnerships and business arrangements that allow for us to mask the underlying infrastructure complexity from data science practitioners, so that they can focus more time and attention on building out the outcomes that the business wants and a sustained business capability so that we can continue to do so. Once again, at the end of this series of conversations, stay with us, so that we can have that crowd chat and you can, again, ask your questions, provide your insights, and participate with the community to help all of us move faster in this crucial direction for better AI, better ML and better analytics. So, the first conversation we're going to have is with Anant Chintamaneni. Anant's the Vice President of Products at BlueData. Anant, welcome to theCUBE. >> Hi Peter, it's great to be here. I think the topic that you just outlined is a very fascinating and interesting one. Over the last 10 years, data and analytics have been used to create transformative experiences and drive a lot of business growth. You look at companies like Uber, AirBnB, and you know, Spotify, practically, every industry's being disrupted. And the reason why they're able to do this is because data is in their DNA; it's their key asset and they've leveraged it in every aspect of their product development to deliver amazing experiences and drive business growth. And the reason why they're able to do this is they've been able to leverage open-source technologies, data science techniques, and big data, fast data, all types of data to extract that business value and inject analytics into every part of their business process. Enterprises of all sizes want to take advantage of that same assets that the new digital companies are taking and drive digital transformation and innovation, in their organizations. But there's a number of challenges. First and foremost, if you look at the enterprises where data was not necessarily in their DNA and to inject that into their DNA, it is a big challenge. The executives, the executive branch, definitely wants to understand where they want to apply AI, how to kind of identify which huge cases to go after. There is some recognition coming in. They want faster time-to-value and they're willing to invest in that. >> And they want to focus more on the actual outcomes they seek as opposed to the technology selection that's required to achieve those outcomes. >> Absolutely. I think it's, you know, a boardroom mandate for them to drive new business outcomes, new business models, but I think there is still some level of misalignment between the executive branch and the data worker community which they're trying to upgrade with the new-age data scientists, the AI developer and then you have IT in the middle who has to basically bridge the gap and enable the digital transformation journey and provide the infrastructure, provide the capabilities. >> So we've got a situation where people readily acknowledge the potential of some of these new AI, ML, big data related technologies, but we've got a mismatch between the executives that are trying to do evidence-based management, drive new models, the IT organization who's struggling to deal with data-first technologies, and data scientists who are few and far between, and leave quickly if they don't get the tooling that they need. So, what's the way forward, that's the problem. How do we move forward? >> Yeah, so I think, you know, I think we have to double-click into some of the problems. So the data scientists, they want to build a tool chain that leverages the best in-class, open source technologies to solve the problem at hand and they don't want, they want to be able to compile these tool chains, they want to be able to apply and create new algorithms and operationalize and do it in a very iterative cycle. It's a continuous development, continuous improvement process which is at odds with what IT can deliver, which is they have to deliver data that is dispersed all over the place to these data scientists. They need to be able to provide infrastructure, which today, they're not, there's an impotence mismatch. It takes them months, if not years, to be able to make those available, make that infrastructure available. And last but not the least, security and control. It's just fundamentally not the way they've worked where they can make data and new tool chains available very quickly to the data scientists. And the executives, it's all about faster time-to-value so there's a little bit of an expectation mismatch as well there and so those are some of the fundamental problems. There's also reproducibility, like, once you've created an analytics model, to be able to reproduce that at scale, to be then able to govern that and make sure that it's producing the right results is fundamentally a challenge. >> Audibility of that process. >> Absolutely, audibility. And, in general, being able to apply this sort of model for many different business problems so you can drive outcomes in different parts of your business. So there's a huge number of problems here. And so what I believe, and what we've seen with some of these larger companies, the new digital companies that are driving business valley ways, they have invested in a unified platform where they've made the infrastructure invisible by leveraging cloud technologies or containers and essentially, made it such that the data scientists don't have to worry about the infrastructure, they can be a lot more agile, they can quickly create the tool chains that work for the specific business problem at hand, scale it up and down as needed, be able to access data where it lies, whether it's on-prem, whether it's in the cloud or whether it's a hybrid model. And so that's something that's required from a unified platform where you can do your rapid prototyping, you can do your development and ultimately, the business outcome and the value comes when you operationalize it and inject it into your business processes. So, I think fundamentally, this start, this kind of a unified platform, is critical. Which, I think, a lot of the new age companies have, but is missing with a lot of the enterprises. >> So, a big challenge for the enterprise over the next few years is to bring these three groups together; the business, data science world and infrastructure world or others to help with those problems and apply it successfully to some of the new business challenges that we have. >> Yeah, and I would add one last point is that we are on this continuous journey, as I mentioned, this is a world of open source technologies that are coming out from a lot of the large organizations out there. Whether it's your Googles and your Facebooks. And so there is an evolution in these technologies much like we've evolved from big data and data management to capture the data. The next sort of phase is around data exploitation with artificial intelligence and machine learning type techniques. And so, it's extremely important that this platform enables these organizations to future proof themselves. So as new technologies come in, they can leverage them >> Great point. >> for delivering exponential business value. >> Deliver value now, but show a path to delivery value in the future as all of these technologies and practices evolve. >> Absolutely. >> Excellent, all right, Anant Chintamaneni, thanks very much for giving us some insight into the nature of the problems that enterprises face and some of the way forward. We're gonna be right back, and we're gonna talk about how to actually do this in a second. (light techno music) >> Introducing, BlueData EPIC. The leading container-based software platform for distributed AI, machine learning, deep learning and analytics environments. Whether on-prem, in the cloud or in a hybrid model. Data scientists need to build models utilizing various stacks of AI, ML and DL applications and libraries. However, installing and validating these environments is time consuming and prone to errors. BlueData provides the ability to spin up these environments on demand. The BlueData EPIC app store includes, best of breed, ready to run docker based application images. Like TensorFlow and H2O driverless AI. Teams can also add their own images, to provide the latest tools that data scientists prefer. And ensure compliance with enterprise standards. They can use the quick launch button. which provides pre configured templates with the appropriate application image and resources. For example, they can instantly launch a new Sandbox environment using the template for TensorFlow with a Jupyter Notebook. Within just a few minutes, it'll be automatically configured with GPUs and easy access to their data. Users can launch experiments and make GPUs automatically available for analysis. In this case, the H2O environment was set up with one GPU. With BlueData EPIC, users can also deploy end points with the appropriate run time. And the inference run times can use CPUs or GPUs. With a container based BlueData Platform, you can deploy fully configured distributed environments within a matter of minutes. Whether on-prem, in the public cloud, or in a hybrid a architecture. BlueData was recently acquired by Hewlett Packward Enterprise. And now, HPE and BlueData are joining forces to help you on your AI journey. (light techno music) To learn more, visit www.BlueData.com >> And we're back. I'm Peter Burris and we're continuing to have this conversation about how businesses are turning experience with the problems of advance analytics and the solutions that they seek into actual systems that deliver continuous on going value and achieve the business capabilities required to make possible these advanced outcomes associated with analytics, AI and ML. And to do that, we've got two great guests with us. We've got Kumar Sreekanti, who is the co-founder and CEO of BlueData. Kumar, welcome back to theCUBE. >> Thank you, it is nice to be here, back again. >> And Kumar, you're being joined by a customer. Ramesh Thyagarajan, is the executive director of the Advisory Board Company which is part of Optum now. Ramesh, welcome to theCUBE. >> Great to be here. >> Alright, so Kumar let's start with you. I mentioned up front, this notion of turning technology and understanding into actual business capabilities to deliver outcomes. What has been BlueData's journey along, to make that happen? >> Yeah, it all started six years ago, Peter. It was a bold vision and a big idea and no pun intended on big data which was an emerging market then. And as everybody knows, the data was enormous and there was a lot of innovation around the periphery. but nobody was paying attention to how to make the big data consumable in enterprise. And I saw an enormous opportunity to make this data more consumable in the enterprise and to give a cloud-like experience with the agility and elasticity. So, our vision was to build a software infrastructure platform like VMware, specially focused on data intensity distributed applications and this platform will allow enterprises to build cloud like experiences both on enterprise as well as on hybrid clouds. So that it pays the journey for their cloud experience. So I was very fortunate to put together a team and I found good partners like Intel. So that actually is the genesis for the BlueData. So, if you look back into the last six years, big data itself has went through a lot of evolution and so the marketplace and the enterprises have gone from offline analytics to AI, ML based work loads that are actually giving them predictive and descriptive analytics. What BlueData has done is by making the infrastructure invisible, by making the tool set completely available as the tool set itself is evolving and in the process, we actually created so many game changing software technologies. For example, we are the first end-to-end content-arised enterprise solution that gives you distributed applications. And we built a technology called DataTap, that provides computed data operation so that you don't have to actually copy the data, which is a boom for enterprises. We also actually built multitenancy so those enterprises can run multiple work loads on the same data and Ramesh will tell you in a second here, in the healthcare enterprise, the multitenancy is such a very important element. And finally, we also actually contributed to many open source technologies including, we have a project called KubeDirector which is actually is our own Kubernetes and how to run stateful workloads on Kubernetes. which we have actually very happy to see that people like, customers like Ramesh are using the BlueData. >> Sounds like quite a journey and obviously you've intercepted companies like the advisory board company. So Ramesh, a lot of enterprises have mastered or you know, gotten, understood how to create data lakes with a dupe but then found that they still weren't able to connect to some of the outcomes that they saw. Is that the experience that you had. >> Right, to be precise, that is one of the kind of problems we have. It's not just the data lake that we need to be able to do the workflows or other things, but we also, being a traditional company, being in the business for a long time, we have a lot of data assets that are not part of this data lake. We're finding it hard to, how do we get the data, getting them and putting them in a data lake is a duplication of work. We were looking for some kind of solutions that will help us to gather the benefits of leaving the data alone but still be able to get into it. >> This is where (mumbles). >> This is where we were looking for things and then I was lucky and fortunate to run into Kumar and his crew in one of the Hadoop conferences and then they demonstrated the way it can be done so immediately hit upon, it's a big hit with us and then we went back and then did a POC, very quickly adapt to the technology and that is also one of the benefits of corrupting this technology is the level of contrary memorization they are doing, it is helping me to address many needs. My data analyst, the data engineers and the data scientists so I'm able to serve all of them which otherwise wouldn't be possible for me with just this plain very (mumbles). >> So it sounds as though the partnership with BlueData has allowed you to focus on activities and problems and challenges above the technology so that you can actually start bringing data science, business objectives and infrastructure people together. Have I got that right? >> Absolutely. So BlueData is helping me to tie them all together and provide an excess value to my business. We being in the healthcare, the importance is we need to be able to look at the large data sets for a period of time in order to figure out how a patient's health journey is happening. That is very important so that we can figure out the ways and means in which we can lower the cost of health care and also provide insights to the physician, they can help get people better at health. >> So we're getting great outcomes today especially around, as you said that patient journey where all the constituents can get access to those insights without necessarily having to learn a whole bunch of new infrastructure stuff but presumably you need more. We're talking about a new world that you mentioned before upfront, talking about a new world, AI, ML, a lot of changes. A lot of our enterprise customers are telling us it's especially important that they find companies that not only deliver something today but demonstrate a commitment to sustain that value delivery process especially as the whole analytics world evolves. Are you experiencing that as well? >> Yes, we are experiencing and one of the great advantage of the platform, BlueData platform that gave me this ability to, I had the new functionality, be it the TensorFlow, be it the H2O, be it the heart studio, anything that I needed, I call them, they give me the images that are plug-and-play, just put them and all the prompting is practically transparent to nobody need to know how it is achieved. Now, in order to get to the next level of the predictive and prescriptive analytics, it is not just you having the data, you need to be able to have your curated data asset set process on top of a platform that will help you to get the data scientists to make you. One of the biggest challenges that are scientist is not able to get their hands on data. BlueData platform gives me the ability to do it and ensure all the security meets and all the compliances with the various other regulated compliances we need to make. >> Kamar, congratulations. >> Thank you. >> Sounds like you have a happy customer. >> Thank you. >> One of the challenges that every entrepreneur faces is how did you scale the business. So talk to us about where you are in the decisions that you made recently to achieve that. >> As an entrepreneur, when you start a company, odds are against you, right? You're always worried about it, right. You make so many sacrifices, yourself and your team and all that but the the customer is the king. The most important thing for us to find satisfied customers like Rameshan so we were very happy and BlueData was very successful in finding that customer because i think as you pointed out, as Ramesh pointed out, we provide that clean solution for the customer but as you go through this journey as a co-founder and CEO, you always worry about how do you scale to the next level. So we had partnerships with many companies including HPE and we found when this opportunity came in front of me with myself and my board, we saw this opportunity of combining the forces of BlueData satisfied customers and innovative technology and the team with the HPs brand name, their world-class service, their investment in R&D and they have a very long, large list of enterprise customers. We think putting these two things together provides that next journey in the BlueData's innovation and BlueData's customers. >> Excellent, so once again Kumar Sreekanti, co-founder and CEO of BlueData and Ramesh Thyagarajan who is the executive director of the advisory board company and part of Optum, I want to thank both of you for being on theCUBE. >> Thank you >> Thank you, great to be here. >> Now let's hear a little bit more about how this notion of bringing BlueData and HPE together is generating new classes of value that are making things happen today but are also gonna make things happen for customers in the future and to do that we've got Dave Velante who's with Silicon Angle Wiki Bond joined by Patrick Osbourne who's with HPE in our Marlborough studio so Dave over to you. >> Thanks Peter. We're here with Patrick Osbourne, the vice president and general manager of big data and analytics at Hewlett Packard Enterprise. Patrick, thanks for coming on. >> Thanks for having us. >> So we heard from Kumar, let's hear from you. Why did HPE purchase, acquire BlueData? >> So if you think about it from three angles. Platform, people and customers, right. Great platform, built for scale addressing a number of these new workloads and big data analytics and certainly AI, the people that they have are amazing, right, great engineering team, awesome customer success team, team of data scientists, right. So you know, all the folks that have some really, really great knowledge in this space so they're gonna be a great addition to HPE and also on the customer side, great logos, major fortune five customers in the financial services vertical, healthcare, pharma, manufacturing so a huge opportunity for us to scale that within HP context. >> Okay, so talk about how it fits into your strategy, specifically what are you gonna do with it? What are the priorities, can you share some roadmap? >> Yeah, so you take a look at HPE strategy. We talk about hybrid cloud and specifically edge to core to cloud and the common theme that runs through that is data, data-driven enterprises. So for us we see BlueData, Epic platform as a way to you know, help our customers quickly deploy these new mode to applications that are fueling their digital transformation. So we have some great plans. We're gonna certainly invest in all the functions, right. So we're gonna do a force multiplier on not only on product engineering and product delivery but also go to market and customer success. We're gonna come out in our business day one with some really good reference architectures, with some of our partners like Cloud Era, H2O, we've got some very scalable building block architectures to marry up the BlueData platform with our Apollo systems for those of you have seen that in the market, we've got our Elastic platform for analytics for customers who run these workloads, now you'd be able to virtualize those in containers and we'll have you know, we're gonna be building out a big services practice in this area. So a lot of customers often talk to us about, we don't have the people to do this, right. So we're gonna bring those people to you as HPE through Point Next, advisory services, implementation, ongoing help with customers. So it's going to be a really fantastic start. >> Apollo, as you mentioned Apollo. I think of Apollo sometimes as HPC high performance computing and we've had a lot of discussion about how that's sort of seeping in to mainstream, is that what you're seeing? >> Yeah absolutely, I mean we know that a lot of our customers have traditional workloads, you know, they're on the path to almost completely virtualizing those, right, but where a lot of the innovation is going on right now is in this mode two world, right. So your big data and analytics pipeline is getting longer, you're introducing new experiences on top of your product and that's fueling you know, essentially commercial HPC and now that folks are using techniques like AI and modeling inference to make those services more scalable, more automated, we're starting to bringing these more of these platforms, these scalable architectures like Apollo. >> So it sounds like your roadmap has a lot of integration plans across the HPE portfolio. We certainly saw that with Nimble, but BlueData was working with a lot of different companies, its software, is the plan to remain open or is this an HPE thing? >> Yeah, we absolutely want to be open. So we know that we have lots of customers that choose, so the HP is all about hybrid cloud, right and that has a couple different implications. We want to talk about your choice of on-prem versus off-prem so BlueData has a great capability to run some of these workloads. It essentially allows you to do separation of compute and storage, right in the world of AI and analytics we can run it off-prem as well in the public cloud but then we also have choice for customers, you know, any customer's private cloud. So that means they want to run on other infrastructure besides HPE, we're gonna support that, we have existing customers that do that. We're also gonna provide infrastructure that marries the software and the hardware together with frameworks like Info Site that we feel will be a you know, much better experience for the customers but we'll absolutely be open and absolutely have choice. >> All right, what about the business impact to take the customer perspective, what can they expect? >> So I think from a customer perspective, we're really just looking to accelerate deployment of AI in the enterprise, right and that has a lot of implications for us. We're gonna have very scalable infrastructure for them, we're gonna be really focused on this very dynamic AI and ML application ecosystems through partnerships and support within the BlueData platform. We want to provide a SAS experience, right. So whether that's GPUs or accelerators as a service, analytics as a service, we really want to fuel innovation as a service. We want to empower those data scientists there, those are they're really hard to find you know, they're really hard to retain within your organization so we want to unlock all that capability and really just we want to focus on innovation of the customers. >> Yeah, and they spend a lot of time wrangling data so you're really going to simplify that with the cloud (mumbles). Patrick thank you, I appreciate it. >> Thank you very much. >> Alright Peter, back to you in Palo Alto. >> And welcome back, I'm Peter Burris and we've been talking a lot in the industry about how new tooling, new processes can achieve new classes of analytics, AI and ML outcomes within a business but if you don't get the people side of that right, you're not going to achieve the full range of benefits that you might get out of your investments. Now to talk a little bit about how important the data science practitioner is in this equation, we've got two great guests with us. Nanda Vijaydev is the chief data scientists of BlueData. Welcome to theCUBE. >> Thank you Peter, happy to be here. >> Ingrid Burton is the CMO and business leader at H2O.AI, Ingrid, welcome to the CUBE. >> Thank you so much for having us. >> So Nanda Vijaydev, let's start with you. Again, having a nice platform, very, very important but how does that turn into making the data science practitioner's life easier so they can deliver more business value. >> Yeah thank you, it's a great question. I think end of the day for a data scientist, what's most important is, did you understand the question that somebody asked you and what is expected of you when you deliver something and then you go about finding, what do I need for them, I need data, I need systems and you know, I need to work with people, the experts in the process to make sure that the hypothesis I'm doing is structured in a nice way where it is testable, it's modular and I have you know, a way for them to go back to show my results and keep doing this in an iterative manner. That's the biggest thing because the satisfaction for a data scientist is when you actually take this and make use of it, put it in production, right. To make this whole thing easier, we definitely need some way of bringing it all together. That's really where, especially compared to the traditional data science where everything was monolithic, it was one system, there was a very set way of doing things but now it is not so you know, with the growing types of data, with the growing types of computation algorithms that's available, there's a lot of opportunity and at the same time there is a lot of uncertainty. So it's really about putting that structure and it's really making sure you get the best of everything and still deliver the results, that is the focus that all data scientists strive for. >> And especially you wanted, the data scientists wants to operate in the world of uncertainty related to the business question and reducing uncertainty and not deal with the underlying some uncertainty associated with the infrastructure. >> Absolutely, absolutely you know, as a data scientist a lot of time used to spend in the past about where is the data, then the question was, what data do you want and give it to you because the data always came in a nice structured, row-column format, it had already lost a lot of context of what we had to look for. So it is really not about you know, getting the you know, it's really not about going back to systems that are pre-built or pre-processed, it's getting access to that real, raw data. It's getting access to the information as it came so you can actually make the best judgment of how to go forward with it. >> So you describe the world with business, technology and data science practitioners are working together but let's face it, there's an enormous amount of change in the industry and quite frankly, a deficit of expertise and I think that requires new types of partnerships, new types of collaboration, a real (mumbles) approach and Ingrid, I want to talk about what H2O.AI is doing as a partner of BlueData, HPE to ensure that you're complementing these skills in pursuit or in service to the customer's objectives. >> Absolutely, thank you for that. So as Nanda described, you know, data scientists want to get to answers and what we do at H2O.AI is we provide the algorithms, the platforms for data scientist to be successful. So when they want to try and solve a problem, they need to work with their business leaders, they need to work with IT and they actually don't want to do all the heavy lifting, they want to solve that problem. So what we do is we do automatic machine learning platforms, we do that with optimizing algorithms and doing all the kind of, a lot of the heavy lifting that novice data scientists need and help expert data scientists as well. I talk about it as algorithms to answers and actually solving business problems with predictions and that's what machine learning is really all about but really what we're seeing in the industry right now and BlueData is a great example of kind of taking away some of the hard stuff away from a data scientist and making them successful. So working with BlueData and HPE, making us together really solve the problems that businesses are looking for, it's really transformative and we've been through like the digital transformation journey, all of us have been through that. We are now what I would term an AI transformation of sorts and businesses are going to the next step. They had their data, they got their data, infrastructure is kind of seamlessly working together, the clusters and containerization that's very important. Now what we're trying to do is get to the answers and using automatic machine learning platforms is probably the best way forward. >> That's still hard stuff but we're trying to get rid of data science practitioners, focusing on hard stuff that doesn't directly deliver value. >> It doesn't deliver anything for them, right. They shouldn't have to worry about the infrastructure, they should worry about getting the answers to the business problems they've been asked to solve. >> So let's talk a little bit about some of the new business problems that are going to be able to be solved by these kinds of partnerships between BlueData and H2O.AI. Start, Nanda, what do you, what gets you excited when we think about the new types of business problems that customers are gonna be able to solve. >> Yeah, I think it is really you know, the question that comes to you is not filtered through someone else's lens, right. Someone is trying an optimization problem, someone is trying to do a new product discovery so all this is based on a combination of both data-driven and evidence-based, right. For us as a data scientist, what excites me is that I have the flexibility now that I can choose the best of the breed technologies. I should not be restricted to what is given to me by an IT organization or something like that but at the same time, in an organization, for things to work, there has to be some level of control. So it is really having this type of environments or having some platforms where some, there is a team that can work on the control aspect but as a data scientist, I don't have to worry about it. I have my flexibility of tools of choice that I can use. At the same time, when you talk about data, security is a big deal in companies and a lot of times data scientists don't get access to data because of the layers and layers of security that they have to go through, right. So the excitement of the opportunity for me is if someone else takes care of the problem you know, just tell me where is the source of data that I can go to, don't filter the data for me you know, don't already structure the data for me but just tell me it's an approved source, right then it gives me more flexibility to actually go and take that information and build. So the having those controls taken care of well before I get into the picture as a data scientist, it makes it extremely easy for us to focus on you know, to her point, focus on the problem, right, focus on accessing the best of the breed technology and you know, give back and have that interaction with the business users on an ongoing basis. >> So especially focus on, so speed to value so that you're not messing around with a bunch of underlying infrastructure, governance remaining in place so that you know what are the appropriate limits of using the data with security that is embedded within that entire model without removing fidelity out of the quality of data. >> Absolutely. >> Would you agree with those? >> I totally agree with all the points that she brought up and we have joint customers in the market today, they're solving very complex problems. We have customers in financial services, joint customers there. We have customers in healthcare that are really trying to solve today's business problems and these are everything from, how do I give new credit to somebody? How do I know what next product to give them? How do I know what customer recommendations can I make next? Why did that customer churn? How do I reach new people? How do I do drug discovery? How do I give a patient a better prescription? How do I pinpoint disease than when I couldn't have seen it before? Now we have all that data that's available and it's very rich and data is a team sport. It takes data scientists, it takes business leaders and it takes IT to make it all work together and together the two companies are really working to solve problems that our customers are facing, working with our customers because they have the intellectual knowledge of what their problems are. We are providing the tools to help them solve those problems. >> Fantastic conversation about what is necessary to ensure that the data science practitioner remains at the center and is the ultimate test of whether or not these systems and these capabilities are working for business. Nanda Vijaydev, chief data scientist of BlueData, Ingrid Burton CMO and business leader, H2O.AI, thank you very much for being on theCUBE. >> Thank you. >> Thank you so much. >> So let's now spend some time talking about how ultimately, all of this comes together and what you're going to do as you participate in the crowd chat. To do that let me throw it back to Dave Velante in our Marlborough studios. >> We're back with Patrick Osbourne, alright Patrick, let's wrap up here and summarize. We heard how you're gonna help data science teams, right. >> Yup, speed, agility, time to value. >> Alright and I know a bunch of folks at BlueData, the engineering team is very, very strong so you picked up a good asset there. >> Yeah, it means amazing technology, the founders have a long lineage of software development and adoption in the market so we're just gonna, we're gonna invested them and let them loose. >> And then we heard they're sort of better together story from you, you got a roadmap, you're making some investments here, as I heard. >> Yeah, I mean so if we're really focused on hybrid cloud and we want to have all these as a services experience, whether it's through Green Lake or providing innovation, AI, GPUs as a service is something that we're gonna be you know, continuing to provide our customers as we move along. >> Okay and then we heard the data science angle and the data science community and the partner angle, that's exciting. >> Yeah, I mean, I think it's two approaches as well too. We have data scientists, right. So we're gonna bring that capability to bear whether it's through the product experience or through a professional services organization and then number two, you know, this is a very dynamic ecosystem from an application standpoint. There's commercial applications, there's certainly open source and we're gonna bring a fully vetted, full stack experience for our customers that they can feel confident in this you know, it's a very dynamic space. >> Excellent, well thank you very much. >> Thank you. Alright, now it's your turn. Go into the crowd chat and start talking. Ask questions, we're gonna have polls, we've got experts in there so let's crouch chat.