 from Las Vegas, it's theCUBE. Cover EMC World 2016, brought to you by EMC. Now, here are your hosts, Stu Miniman and Brian Graceley. Welcome back to Las Vegas, the CUBE's coverage of EMC World 2016. I'm Stu Miniman joined by Brian Graceley. Happy to have on the program for the first time, Ted Bardaz, who's the senior director of product management in the EMC Converge platform division. You're working on some of the big data solutions. Ted, welcome to the program. Well, thanks very much. Thanks for having me, I appreciate it. So, you know, we at Wikibon on theCUBE, we've been covering big data since it was before it was called big data. EMC, of course, everybody knows kind of the storage pieces underlying it, but give us a little bit about your role and what your team does today. Okay, sure. So, our role really for the big data solution is all about delivering a fully engineered and term key platform to really empower companies to get insights out of their data, whether in the enterprise and even external stores, to be able to get insights to improve their business performance, right? To really get business uplift in there, whether that's, you know, additional services or products, better customer experience, reducing costs or operational efficiency. So, if you think about the stack, we have the data lake that can be Icelon or ECS. We have big data systems, the converged infrastructure, the racks and blocks, and then we really have the solution which is really all about the user experience that's really empowering both the data scientists and IT to be able to deliver business value to the business stakeholders and the company. Yeah, so when I look, you know, we've got a lot of experience with EMC solutions and on the VCE side, they really productized a lot of the solutions. Can you talk a little bit about, you know, what does that mean going together? You're all under kind of this umbrella that used to be VCE. We've talked to the pivotal folks. We're talking to the hybrid cloud people. How does your group fit into that whole narrative? It's a, so it's really about combining the different assets and especially in big data, EMC really has, you know, a very strong position in terms of the technology and IP around big data. So really one of the key roles that we have is to take those assets, take the data lake technologies, take the pivotal big data suite, take the converged infrastructure and really focus on the key business goals of the end user and weave those technologies together into a really compelling user experience. That's one that's driving business objectives, right? One that's delivering up against a lot of companies and especially here at EMC World, they're really looking for, how do they become data-driven companies and how do they mix that into how they execute? A lot of the business is changing around them. I was speaking to a company last night, their market was shrinking, they realized they needed to do business and generate a different business model and they really were approaching and saying, how can you help us leverage big data and leverage the data that we have to really allow us to pivot so we can compete effectively? So we wrapped those technologies together in order to make that very consumable and allow businesses to attack those kinds of business objectives. Obviously, you know, typical engagement, you guys are going to stand up the environment, make it very, very easy to stand up for IT, give an environment for the data scientists to get to working, what goes on beyond that? How do you help them, especially for that customer that says, what more can you do for me? What's a typical engagement look like and what are some of the outcomes they're starting to see? Okay, so it's great that you picked up on it because we have the data lake, we have the data systems, the big data systems and I mentioned the big data solutions, but we also have our big data services and our professional services that really has programs that go in as we engage a particular customer that has a big data workshop where they sit with the customer for a week or so and they really sit down and identify what's possible and what are really nice high value targets that they can go after. They take that to a proof of value concept where they actually bring in the data scientists, they bring in the data and they actually prove out the delivery, the ability to take those insights and really to deliver a positive business value and then they stay with that customer to bring them into production. So one of the ones just before I came here, we had Pichanga, which is a customer who purchased our big data solution. They're at Casino in Temecula and they've brought the business data lake in and really looking at one of their key objectives was how do they understand what they call their gamer? How do they understand their gamers, what they're playing, how they're playing, so that they can really engage them to give them a better experience, give them additional products and then also get them back because a gamer always has a choice to what casino they can go to and then the area, right, there's choices there. So they really, as an example, they really wanted to get deep into the analytics on the information that they had from their game card, from their gamers and to be able to understand how they better serve that customer and then how they get them to come back. Can you unpack for us the key stakeholders in the environment, who's driving the engagement, who's working on the engagement, who benefits from it? Okay, so I think there's really three key constituents. I think the real drivers that we're seeing as we're getting into the workshop is really coming from the business and the business vision area. So like with Pichanga and like with other customers, there's someone sitting on the business side that's really thinking about particular outcomes that they think they believe they should be able to achieve based on what they've heard about big data. There's quickly a marriage that occurs between that and the IT teams, right? Because it isn't, hey, let's just do big data, right? You have to set up your environment, you have to create the right environment for that, both in terms of setting up the right infrastructure to run these big data tools, but also in terms of setting up the environment in order to empower the data scientists, right? So our key, a typical engagement, we'll find a business that's going to approach us in terms of, hey, we think we have some ideas on how we think we should be able to use big data, get the workshop going, quickly transfers over to the IT delivery, IT ops and the IT team in terms of what is the environment and what really needs to be stood up. And then the third thing, and I know I really enjoy the Wikibon reports, I'll give you a throw there, but you know, the dearth of data scientists, right? Even as we were doing the Pachanga talk, you know, they had said, hey, if you're going to go after this, recognize you're going to need lead time in order to get good data scientists. So it starts with the business value, quickly comes up to the infrastructure and the IT visionary married to the business visionary, but with a key focus on really empowering data scientists so they can really ring the value, the insights that are going to deliver value to the business, and those are the key constituents. Yeah. Big data, the term, the concept has gone through a lot lately. You mentioned data scientists, you know, for a while, very, very hard to find, very much in demand. We're seeing technology changes, you know, Hadoop to Spark, real time. But what part of big data has sort of, you know, done, solved problem and what part is still sort of challenging for customers? So I think that it's really kind of a very interesting market and that it's nascent, right? That technologies are changing, Hadoop, Spark, you know, what's going to be next. I think the key thing and some of the key challenges and some of the things that we're looking at are really one is empowerment and self-service of the data scientists. So one, how do we just get it so that when they have that insight that they want to go after that there's not, you know, 15 different tickets to IT that they're waiting for and then eventually maybe they go out and do shadow IT. So one of them is just getting the environment set up. But some of the other ones are really around the data and curating the data in order to apply the analytics too. And so, you know, the statistic, you know, 50 to 80% of a data scientist's time is spent, you know, finding, blending, curating, cleansing the data in order to run the analytics. So a lot of what we're looking at is really both the self-service environment, so that data scientists is really empowered to get going with an insight in the data sources to be able to understand them. And with what they want to achieve, stand up that environment. But then give them a very efficient path in order to understand how the data's coming together, how to model it and look at your analytic models, be able to connect to additional data sources, sample them, take a look at how it completes your model and take that through the process so you can't apply your analytics. The analytics are changing so fast that you can't just focus, you can focus on one. But, you know, I think what's more important is really focusing on the environment that allows it to be stood up and allowing them to prepare the environment with the data that the analytics really want to run on. So Ted, looking forward, what do we expect to see kind of down the road from the EMC Big Data Solutions team? So I think pretty quick, you know, as we just talked about the ingestion and the data curation, you'll see some really nice work with some partners that we have. And so we're targeting, you know, sometime around Strata Hadoop, New York to really bring out some of these capabilities. We're deploying them now in a services-led opportunity so that we can really learn from customers and real-world use cases. So what goes in the box is reflective of what really happens. Secondly, I think that a lot of the customers who want to leverage Big Data have asked us to flex into the cloud. So really taking our solution and putting it on our hybrid cloud environments to be able to allow them to flex out, to be able to run their critical, really data proprietary or connection on-prem, but still allow empowerment to Big Data, but if it's going to interfere with that mission critical work to be able to support them out in the cloud. And then third, it's about, you know, insights are great, but it's about actioning the insights. So the third thing is really blending in with our native hybrid cloud capabilities so that you can have your data scientists on one side who are self-empowered, can quickly connect to the data, bring it into the environment, see these insights that are going to deliver business value, but then marrying those data services that data scientists is using to over to native hybrid cloud so that you can take those and actionalize them, right? You can build an embedded system in your operational model. Let's say if you're doing predictive maintenance in an oil field and you want to tie that into a service truck to go out and service a well, you know, based on that. Also dashboards, of course, and reports for insights on strategic business decision-making, but then also really a lot more that we're seeing customers really gravitating to is how do they reach that end-user customer? How do they get that mobile app? How do they get that application that really creates a much tighter relationship, right, with their customers that's really targeted at how they buy, what they buy, when they buy on the devices, you know, anywhere, anytime. The culture, right, that's happening right now is people are expecting immediate response. And the notion of building these insights and getting some more of this real-time capability into a platform that can deliver cloud-native applications that can really reach the customers and satisfy that sort of frequency or that rate that they want to run at, that's going to be key, and that's what a lot of customers are looking for. All right, well, Ted Bardas, appreciate you sharing with us how we're closing the gap from just storing information to actionable insights. I look forward to talking with you more in the future. We'll be back with more coverage here from EMC World 2016. You're watching theCUBE.