>>The cube presents HPE discover 2022 brought to you by HPE. >>Hello, everybody. Welcome to day three of the Cube's coverage of HPE discover 2022 we're live from Las Vegas and the Venetian convention center. This is I, I counted him up. I think this is the 14th HP HP slash HPE. Discover that we've done really excited to welcome in Alexia Clements. She's the vice president of go to market for HPE GreenLake cloud services. That's all the rage everybody's talking about. Green, all the wood behind the arrow, as the saying goes, welcome to the queue. Good to see >>You. Thank you so much for having me thrilled to be here. >>You walk up Janet Jackson last night, >>Epic. Wow. She killed it. She was awesome. >>I thought the band was super tight, but the other thing was the place was >>Packed. It was >>Nice. You know, what happens is a lot of time they put the band in the getaway day, you know, and nobody stays, but wow, the, the hall was jammed. >>It was great. It was, you could feel the momentum and the excitement. And it was just a great way to, to kind of end the, the HP discover. So it was great. >>Yeah. I mean, I, I mentioned that we've been to a lot of HP slash HPE discovers and, and this one was different in the sense that I think first of all, 8,000 people, yep. People are excited to get back together, but I think, you know, HPE has a spring in its step and the customers are kind of interested. It's much more focused than some of the past HPE discoverers, which was kind of hard to get my hands around. Sometimes the business was sort of an Antonio's pulled that together. So what's changed since the last time we were face to face. >>We're transforming and hope you all saw that on the, on the floor here. So, um, we're absolutely trans going through a transformation and, you know, I, I think we're, you know, we're shifting to an edge to cloud platform company. And with that, it's, it's how we approach our customers differently and our partners and, you know, we're hoping that, uh, we showed this week and that, that we're different and we're transforming. >>So how do you spend your time Mo mostly in front of customers having conversations about what, what their needs are and aligning is that right? >>Yeah. So, um, I, I lead the, the go to market for GreenLake. So that's everything around how we're driving our as a service go to market strategy, how we're driving programs, enablement, how we're really in the end, how we're executing on that as a service strategy from a sales perspective. >>So what do you hear? Of course, a lot of that involves partners. Yep. Right. I mean, that's kind of the route to market. Absolutely. The HPE prefers for obvious reasons, although others don't necessarily share that, but, but, so what are you hearing from the partner ecosystem and the customers that their biggest challenges are now that we're entering the let's call it the post isolation economy? <laugh> >>Yeah. I mean, the reality is, is digital transformations are hard and I think some customers, um, who haven't necessarily moved forward on it or, you know, maybe they move forward and they're realizing, Hey, I'm stuck and I'm not, I'm not getting to where I wanna be and really, you know, driving that end state. So, I mean, I, I would just say overall, I think things are like, customers are, are struggling if they didn't, you know, they're falling behind a little bit. And I think through the conversations that we're having and through HP green, like it gives customers choice. And so really, um, I mean, what, you know, I spend my time with, and, and when we're talking to customers and partners, it's about helping customers on that digital transformation journey and understanding what are they trying to drive? What business outcomes are they trying to drive and how we can help them get there. So >>I, I often call it the force March to digital yep. With the pandemic. Um, and, and I, I was looking at a survey recently, I think it was put on by couch base. And it was probably on a thousand respondents and it was a CIO survey and they asked who's, who's responsible for the digital transformation at the organization and overwhelmingly it was the it organization. And I said, uhoh, that's the problem now. But it made sense to me because when the economy shut down, everybody went to it and said help, right. Make this work somehow. Right. But, but what, that doesn't seem to me to be the right prescription for a successful digital transformation. Do you agree with that? And what do you see as a successful template for DX? >>Well, I think what, what we see is that really the lines of business are desperate to move fast and they're really looking for their it partners to help them in that journey and, and, and drive, you know, whether it be, you know, drive them, you know, drive orders, drive, you know, they need it to help them in that journey. And so really it's gotta be a partnership between the two organizations. And what we're trying to do with HP GreenLake is kind of abstract that almost. So, Hey, we're gonna give it to you in an, as a service and you're gonna get all of these components. And all you have to think about is where do I need to grow and what are the outcomes that I'm looking for? So that's what it's gotta be. There's gotta be tight alignment, I think between the lines of business and it, and sometimes those two don't know how to talk to each other. >>Mm-hmm <affirmative> so that's another way of, of really trying to speak to the business leaders and say, what are you trying to do? Where do you need to go? And what do you need to get? And, and a lot of times they don't even know what they need to get there. So that's where we need to have those different conversations with our customers to, and that's where we look for our partners to help us in that. So really having those different conversations to progress, um, what, you know, what customers are really looking to, to drive, >>How, how does GreenLake specifically accelerate that transformation? Where does it fit? Maybe you can kind of take us through, you know, a, a generic example of how that works. >>Yeah. I mean, a great example is, you know, especially with the pandemic is desktop, Hey, you now need to, you know, everybody's working from different locations. So, you know, desktop as a service VDI as a service, and, you know, you're putting it in a, you know, per whatever, you know, per you can, whatever variable pricing you want, but think about it, you have that one pay as you go. And so the it organization, all they have to think about is that's my, you know, per, per unit price there. So that's a great example of how we saw, like, especially during the pandemic, that was something that was, you know, a huge area of focus organizations. What's >>The spectrum that you see in terms of, you know, the maturity model, if you will, a digital transformation. I mean, if you weren't in a digital business during the pandemic, you were pretty much out of business. Yeah. And with very few exceptions. Um, and so, okay. So on the one end, you have folks that sort of were forced into it. You, my forced March scenario, others were actually moving quite a bit along before the pandemic, others were kind of given at lip service and maybe doing a few projects. What do you see as that spectrum? >>I think if you're not transforming, you're falling behind. And so everybody needs to be, you know, looking to the future and understanding, you know, really trying to get aggressive on that. And that's what we're seeing. We're seeing companies who, you know, aren't moving fast on that or falling behind. >>Do you see a bifurcation? I'm sure you do those that say, yeah, I want as a service and others that say, look, I I'm really well capitalized. I'm gonna gimme the, gimme the CapEx. I'm gonna put it in and run it myself. And is there a relationship between that approach and their digital transformation maturity, or is it kind of just really their preference? >>I, I mean, for us, we're meeting customers where they're at on their journey and their multi-cloud journey. So some, and, and what I'm seeing is that every customer today has multiple clouds, whether that be their, you know, their kind of, MultiGen it, the, the legacy stuff that they've gotta deal with. They've got stuff in public clouds, and they're trying to really transform and figure out how do I work all of that in like, how do I move forward with that new operating model? And so what I'm seeing is, you know, we're gonna meet customers where they're at on their journey. So some are gonna continue to go down that path in a, how they've always purchased their it. And others are really, you know, more often than not, we're seeing, they want that as a service cloudlike to have all the benefits of cloud, but yet still have it on their prem or in a colo or, you know, at the edge. So I do see some of those customers who are thinking differently, right. That, and they're the ones that are more apt to be a little bit more aggressive on their digital transformation. They're, they're open to the possibility if that makes sense. No, >>It does. It makes total sense. I, I, I think, you know, on the one hand they're a lot of customers are trying to build their own cloud. Yep. Um, so you mention multicloud, I'm not gonna go to Amazon to help me with my multicloud strategy. That's not, that's not gonna be my preferr. Yeah. I might talk to Microsoft about it a little bit. Google's got Antos and that's kind of interesting, but you know, Google's not enterprise, they got good data, but so, but there are other choices out there. Why HPE for my cloud hybrid multi-cloud strategy, give us the >>Sticker. It's, it's the best of both worlds for customers. So it enables them to have the security. It enables them to grow, to, to be in their data centers or in colos at the edge. It allows them to not over provision. It allows them to pay as they go and pay as they grow there's. Um, and then it also really is that ease factor. So it it's that thinking about it as I have, I already, I know what my pricing is. I know what that predictability is from a pricing perspective and what my costs are gonna be. So all of those things really re that all those messages resonate with customers, >>Right? L thanks so much for coming on. We got the trains are backing up super tight schedule today. This is wall to wall coverage of HPE. Discover. Thank you. Thank >>You so much for having me appreciate it. >>You're SU very welcome. All right. Keep it right there. Dave ante is here. John furrier, HPE discover 2022 from Las Vegas. We're live. We'll be right back.

(bright upbeat music) >> Welcome back to theCube's coverage of HPE's Green Lake announcement. We've been following the caves of Green Lake's announcement for several quarters now, and even years. And we're going to look at cloud adoption and frameworks to help facilitate cloud adoptions. You know, in 2020, the world was on a forced march to digital and there was a lot that they didn't know. Big part of that was how to automate, how to reduce your reliance on physically, manually and plugging things in. And so, customers need an adoption framework to better understand and how to de-risk that journey to the cloud. And with me to talk about that are Alexia Clements, who's the Vice President at Worldwide go to market for GreenLake cloud services at HPE and Alexei Gerasimov who's the vice president of Hybrid Cloud Delivery advisory and professional services at Hewlett Packard Enterprise. Folks, welcome to theCube. >> Alexia: Thanks so much for having us. >> You're very welcome. So, Alexei, what is a cloud adoption framework? How does that all work? >> Gerasimov: Yeah, thanks Dave. So the framework is a structured approach to elevate the conversation, to help our customers get outcomes. So we've been helping customers adopt the benefits in the most of IT for a decade. And we've noticed that they basically focus on eight key areas as they transform to cloud-like capabilities. It's a strategy and governance, it's innovation, people, a dev ops applications, operations security, and data. So we've structured our framework around those core components to help our customers get value. Because end of the day, it's all about changing the way they operate. To get the advantage of all of it. >> Yes. So you can't just pave the cow path and kind of plug your existing process. There's a lot that's unknown, as I said up front. So, so Alexia, maybe you could talk a little bit more about some of the real problems that you're solving with customers that you see in the field. >> Alexey: Yeah, absolutely. So most customers are going through some form of digital transformation and these transformations are difficult and they need a structured approach to help them through that journey. I kind of like to think of it as a recipe to make a meal. So you need to know what ingredients to buy and what are the steps to perform to make that meal. >> Okay. So when you talk to customers, what do you, what do you tell them? That's in it for them after the, after you've actually successfully helped them deploy? What are they telling you? >> Yeah, well, they're telling they now have reached their business outcomes and they're, you know, they're a more agile organization. >> What's the experience look like when you, when you go through one of these journeys and you, you apply the adoption framework, can you sort of paint a picture for us? >> Yeah, absolutely. So every customer is in some sort of transformation, like Alexia said, that transformation implies you've got to know where you start and again, know where you're going. So the experience traditionally is customers need to understand what are my current hybrid cloud capabilities? What do I have, what am I missing? What's lacking and then determine where do you want to go? And in order to get from point A to point B, they have to get a prescriptive approach. So the framework sort of breaks down their path from where they are to their desired maturity. And it takes them in the very prescriptive path to get there. >> So you start with an assessment, you do a gap analysis based on their skill sets. I presume you identify what's possible, help them understand, you know, best practice, which they may not achieve, but this is kind of their north star. Right? And then do you help? How do you help them fill those gaps? Because are skills gaps. Everybody talks about that today. You guys presumably can provide additional services to do that, but so can you add a little bit color to that scope? >> Yeah, yeah, absolutely. And so to your point, the first is a maturity level. So once you figure out the maturity level, you understand what needs to be done. So if you look at our domain, the eight domains that I mentioned and the framework, people is a big one, right? Most of the folks are struggling with people's skills and organizational capabilities. And it's so because it's an operating model change, right? And people are the key component to this operating model change. So we help our customers figure out how do we achieve that optimal operating level and operating a model maturity. And that could be on-prem that could be on public cloud. That could be hybrid. That could be at the edge. And yeah, we, if we can HP, the framework, by the way is pretty, pretty open and pretty objective. If we can help our customers address and achieve their sales gaps great. If we can not directly, then we can have a partner that can help them, you know, plug in something that we don't have. >> Are you finding that, that in terms of the maturity that most people have some kind of experience with, with cloud, but they're struggling to bring that cloud experience to their on-premise state. They don't want to just shove everything into the cloud. Right. So, what does that kind of typical journey look like for folks? I know there's--it's a wide spectrum, or you've got people that are maybe more mature. Maybe some of the folks in financial services got more resources, but can you sort of give us a sense as to what the typical, the average. >> Oh yeah yeah yeah, absolutely. By the way. So that give you a customer example, perfect example of a large North American integrated energy company. They decided to go cloud fresh, like a lot of companies. that wants to do cloud first. And why? The reason was agility. So they started going to the cloud and they realized in order to get agility, you can't just go to you, pick your public CSP, you got to change the way to operate. So they brought us in and they asked, could you help me figure out how we can change the organization? So we actually operate on the proper level of maturity. So we brought our team in. We help them figure out what do we need to look at? We need to look at operations. We need to look at people. We need to look at applications, and we need to figure out what gives you the best value. So when all said and done, they realized that their initial desire of, you know, public first or cloud first, wasn't really public cloud first. It's a way to operate. So now the customer is in three different public CSPs. They're on-prem, there are at edge and everywhere. So that's the focus. Yeah. >> Is the scope predominantly the technical organization. How deep does it go into the, to the business? Is it obviously the application development team is involved, but how deep into the business does this go? The framework. >> Right, and it's absolutely not a technology focused, the whole concept areas, it's outcomes based, and it's a results based. So if you look at the framework, there's really not a single element of the framework that says tech, like storage or compute. No, it's its people, its data, it's business value, strategy and governance, because the goal for us is being objective is we're just trying to help them address the outcomes. Not necessarily to give them more tech. >> So Alexia, I like that answer because it's a wider scope as, I mean, if we just focused on the tech and that's the swim lane, it'd be a lot easier. But as we all know, it's the people in the process that are really the hard part. So that, that makes the challenge for customers greater. You're hurting more cats. So what are the, some of the obstacles that potentially you help customers before they dive in understand. >> Yeah. So we're giving them a roadmap on where they need to go. So we're like I mentioned that recipe, so we're really trying to identify what is their strategy and where do they, what are the outcomes that they're trying to drive and help them on a street, you know, with that path to meet those outcomes. So some of those, I mean, every customer's a little bit different. I mean, we had one customer, which was a, one of the largest hospitals in north America and they, they would needed to, they wanted to go to the cloud, but they realized they couldn't put all of their patient data on the cloud. So what we did was we helped them in changing their operating model and really look to see how does that, how do they need to what's that end game for them, and actually help redo their operating model to have some in the cloud and some on-prem and, and really identify, you know, where they needed to go for their roadmap. So that was an obstacle that they had, hey, we can't put all this stuff out there. How does that now need to work in this new world? >> I would think the data model is a big deal here. I mean, you just gave an example where there's a, there's a, there's a governance and compliance aspect to it. So thinking about that example, did they have to change the way in which they provided federated governance was that presumably identify whose whose responsibility that was to adjudicate, but also had to get the, the implementers to follow that's the, how does that all work? Is it just the deep conversations? And then you figure out how to codify it or. >> No. So what so we have, so through those eight domains that Alexia mentioned, we go through, step-by-step how they need to think about it. And within mind, what are their business outcomes and goals that they're trying to achieve? So really identifying how they need to change that operating model to meet those business outcomes. >> So what's the output, it's a plan, right. That's tailored to the customer. Is that, is that correct? And, and then sort of assistance in implementing downstream or what do they get? >> Yeah, yeah, absolutely. Just to piggyback to what Alexia said, the alignment, the early alignment, the strategy and governance, as you mentioned, this is probably the most important thing, because everybody says we want to be cloud first, but what does that mean? Cloud first means different things to everyone. So we said, give him a plan. The first we'll help with figure out is what does that mean for you? Because at the end of the day, you're not going to the cloud for the sake of cloud, or anywhere you go into the cloud to get some sort of value. So what's that alignment. So the plan is supposed to help you on your road to that value, right? So we'll help them figure out what I want to do, why, for what purpose, what's going to actually address my business value. So yes, they will get a plan as part of it. But more importantly, they get, they get a set of activities, communication plans, which by the way, another block that you got to address. >> Dave: Huge. >> Yeah. >> Yeah. I mean, a lot of executives tell me, look, if you don't change your operating model and go to the cloud, yeah. You're talking, you know, nickels and dimes. If you want to get telephone numbers, you know, big companies, you want to get into bees with billions, you have to change the operating model. And the problem that they tell me is a lot of times the corner offices, okay, we're doing this, but everybody in the fat middle says, what are we doing? >> Right. And now more than ever, I mean, customers need to look at that model like a more modern operating model to realize the benefits of cloud capabilities, whether that be at the edge, their data centers, their colos cloud. So they really need to look at that. And what we've seen is with our framework, we're really helping customers accelerate their business outcomes. De-risk their transformation, and really optimize that cloud operating model. >> It's that alignment you reducing friction within the organization, confusing confusion. When people don't know which direction they're going, they'll just going to go wherever they're pointed. Right. Right. >> And you back to the alignment. So you've got alignment and you mentioned communication. You have to communicate up and down and left and right across the organization because that's one of the most probably ignored elements of any transformation lots of people don't know. So you got to communicate. And then you have to actually measure and report on how they, you know, how the transformation is happening. So we can help in all three of those. >> Especially when everybody's remote. Yeah. Right. And then I said, hey, these digital transformations, there's so much, that's unknown. >> Alexia: Right. It's difficult. >> It's a lot of new. And so you also have to, I presume part of the plan is, Hey, you're not, it's not going to be a hundred percent perfect. So you have to have. >> Alexia: Right. And you're constantly iterating on that plan. >> What does this have to do with GreenLake? >> Alexia: Yeah. So, I mean, GreenLake is HPE's you know, cloud everywhere. And what we're really doing is this framework is helping customers with that path to get that cloud-like experience and as a service model. And so the framework is really helping clients understand where do they need to go and what GreenLake solutions can help them get there. >> So the fundamental assumption of not every cloud player necessarily bad, I would say most hyperscalers is, hey, ultimately, all the data and the workloads are going to go to the cloud, that's their operating premise. So they all have an operating framework to facilitate that. >> Alexia: Right. >> It's, it's tongue in cheek, but it's true. So, but everybody has one of these. How was yours different? >> Yeah. So like, like you said, there's lots of different, you know, frameworks out there, but what we're really focused on is meeting those business goals and outcomes for clients. So we didn't focus on the technology. Like we mentioned what we were really focusing around. I mean, we kind of learned early on that every customer has technical capabilities, applications, data in multiple clouds, on-prem in colos and at the edge. So we didn't focus on like just the technology. So it's really driving business outcomes and their goals and, and the tech, all those frameworks that we just mentioned, they're really specifically driving a particular technology tool or vendor implementing a particular technology or vendor. >> So we've talked about outcomes a lot, but I wonder if we could peel the onion on that. So, you know, the highest level outcome is I want to increase revenue, cut costs, drop to the bottom line, increase shareholder value, improve employee experiences and retention, make customers happier, grow my business. I mean, those are, I mean, I, I don't know a lot of businesses that don't... >> Alexia: Right. >> want to do that, So. Okay. That's cool. But then I'm imagining you really start to peel the layers and say, okay, this is how we're going to get there. And you get down to specific objectives as to the, how is that sort of how this works? >> Right, and that's due to echo at Alexia. So that's exactly why ours is different. We're not focusing on how to adopt Microsoft or AWS or Alibaba with focusing on how we can deliver the customer experience or a better revenue, you know, or, you know, increase the value for the consumer for whatever the company will help him. So the framework we'll look at that and figure out how do we actually address it, whether it's on public cloud, whether it's on prem, whether it's at the edge. >> You mentioned Alexia, that something, hey, if we don't have the skills, we can get a partner who does, a big company. You got a huge partner network. So for example, if you might not have necessarily a deep industry expertise, that's where you might lean on a partner or is that, is that a good example or is there a better one? >> Yes and we know. We're not going to just like you mentioned AWS or Microsoft, Alibaba thing that everything will go to public cloud. I don't believe so, but at the same time we know not everything will stay on-prem. So the combination of on-prem, the edge, you know, private cloud and public cloud is what the customers are after. So our partners could be either third party, system integrator that can help us implement something or even the public CSPs, because we know our customers have capabilities everywhere. So the question becomes, how can we holistically address their needs, whether it's on-prem, whether it's in public cloud. >> Great. Guys, thanks so much. >> Alexia: Thank you. Thanks for having us. Appreciate it. >> My pleasure and thank you for watching everybody's as theCube's continuous coverage of HPE's GreenLake announcement, keep it right there for more great content. (bright upbeat music)

>> Announcer: Live from midtown Manhattan, it's the Cube. Covering Big Data New York City 2017. Brought to you by SiliconANGLE Media and its ecosystem sponsors. >> Welcome back everyone, we are here live in New York City, in Manhattan for BigData NYC, our event we've been doing for five years in conjunction with Strata Data which is formerly Strata Hadoop, which was formerly Strata Conference, formerly Hadoop World. We've been covering the big data space going on ten years now. This is the Cube. I'm here with Aaron Kalb, whose Head of Product and co-founder at Alation. Welcome to the cube. >> Aaron Kalb: Thank you so much for having me. >> Great to have you on, so co-founder head of product, love these conversations because you're also co-founder, so it's your company, you got a lot of equity interest in that, but also head of product you get to have the 20 mile stare, on what the future looks, while inventing it today, bringing it to market. So you guys have an interesting take on the collaboration of data. Talk about what the means, what's the motivation behind that positioning, what's the core thesis around Alation? >> Totally so the thing we've observed is a lot of people working in the data space, are concerned about the data itself. How can we make it cheaper to store, faster to process. And we're really concerned with the human side of it. Data's only valuable if it's used by people, how do we help people find the data, understand the data, trust in the data, and that involves a mix of algorithmic approaches and also human collaboration, both human to human and human to computer to get that all organized. >> John Furrier: It's interesting you have a symbolics background from Stanford, worked at Apple, involved in Siri, all this kind of futuristic stuff. You can't go a day without hearing about Alexia is going to have voice-activated, you've got Siri. AI is taking a really big part of this. Obviously all of the hype right now, but what it means is the software is going to play a key role as an interface. And this symbolic systems almost brings on this neural network kind of vibe, where objects, data, plays a critical role. >> Oh, absolutely, yeah, and in the early days when we were co-founding the company, we talked about what is Siri for the enterprise? Right, I was you know very excited to work on Siri, and it's really a kind of fun gimmick, and it's really useful when you're in the car, your hands are covered in cookie dough, but if you could answer questions like what was revenue last quarter in the UK and get the right answer fast, and have that dialogue, oh do you mean fiscal quarter or calendar quarter. Do you mean UK including Ireland, or whatever it is. That would really enable better decisions and a better outcome. >> I was worried that Siri might do something here. Hey Siri, oh there it is, okay be careful, I don't want it to answer and take over my job. >> (laughs) >> Automation will take away the job, maybe Siri will be doing interviews. Okay let's take a step back. You guys are doing well as a start up, you've got some great funding, great investors. How are you guys doing on the product? Give us a quick highlight on where you guys are, obviously this is BigData NYC a lot going on, it's Manhattan, you've got financial services, big industry here. You've got the Strata Data event which is the classic Hadoop industry that's morphed into data. Which really is overlapping with cloud, IoTs application developments all kind of coming together. How do you guys fit into that world? >> Yeah, absolutely, so the idea of the data lake is kind of interesting. Psychologically it's sort of a hoarder mentality, oh everything I've ever had I want to keep in the attic, because I might need it one day. Great opportunity to evolve these new streams of data, with IoT and what not, but just cause you can get to it physically doesn't mean it's easy to find the thing you want, the needle in all that big haystack and to distinguish from among all the different assets that are available, which is the one that is actually trustworthy for your need. So we find that all these trends make the need for a catalog to kind of organize that information and get what you want all the more valuable. >> This has come up a lot, I want to get into the integration piece and how you're dealing with your partnerships, but the data lake integration has been huge, and having the catalog has come up with, has been the buzz. Foundationally if you will saying catalog is important. Why is it important to do the catalog work up front, with a lot of the data strategies? >> It's a great question, so, we see data cataloging as step zero. Before you can prep the data in a tool like Trifacta, PACSAT, or Kylo. Before you can visualize it in a tool like Tableau, or MicroStrategy. Before you can do some sort of cool prediction of what's going to happen in the future, with a data science engine, before any of that. These are all garbage in garbage out processes. The step zero is find the relevant data. Understand it so you can get it in the right format. Trust that it's good and then you can do whatever comes next >> And governance has become a key thing here, we've heard of the regulations, GDPR outside of the United States, but also that's going to have an arms length reach over into the United States impact. So these little decisions, and there's going to be an Equifax someday out there. Another one's probably going to come around the corner. How does the policy injection change the catalog equation? A lot of people are building machine learning algorithms on top of catalogs, and they're worried they might have to rewrite everything. How do you balance the trade off between good catalog design and flexibility on the algorithm side? >> Totally yes it's a complicated thing with governance and consumption right. There's people who are concerned with keeping the data safe, and there are people concerned with turning that data into real value, and these can seem to be at odds. What we find is actually a catalog as a foundation for both, and they are not as opposed as they seem. What Alation fundamentally does is we make a map of where the data is, who's using what data, when, how. And that can actually be helpful if your goal is to say let's follow in the footsteps of the best analyst and make more insights generated or if you want to say, hey this data is being used a lot, let's make sure it's being used correctly. >> And by the right people. >> And by the right people exactly >> Equifax they were fishing that pond dry months, months before it actually happened. With good tools like this they might have seen this right? Am I getting it right? >> That's exactly right, how can you observe what's going on to make sure it's compliant and that the answers are correct and that it's happening quickly and driving results. >> So in a way you're taking the collective intelligence of the user behavior and using that into understanding what to do with the data modeling? >> That's exactly right. We want to make each person in your organization as knowledgeable as all of their peers combined. >> So the benefit then for the customer would be if you see something that's developing you can double down on it. And if the users are using a lot of data, then you can provision more technology, more software. >> Absolutely, absolutely. It's sort of like when I was going to Stanford, there was a place where the grass was all dead, because people were riding their bikes diagonally across it. And then somebody smart was like, we're going to put a real gravel path there. So the infrastructure should follow the usage, instead of being something you try to enforce on people. >> It's a classic design meme that goes around. Good design is here, the more effective design is the path. >> Exactly. >> So let's get into the integration. So one of the hot topics here this year obviously besides cloud and AI, with cloud really being more the driver, the tailwind for the growth, AI being more the futuristic head room, is integration. You guys have some partnerships that you announced with integration, what are some of the key ones, and why are they important? >> Absolutely, so, there have been attempts in the past to centralize all the data in one place have one warehouse or one lake have one BI tool. And those generally fail, for different reasons, different teams pick different stacks that work for them. What we think is important is the single source of reference One hub with spokes out to all those different points. If you think about it it's like Google, it's one index of the whole web even though the web is distributed all over the place. To make that happen it's very important that we have partnerships to get data in from various sources. So we have partnerships with database vendors, with Cloudera and Hortonworks, with different BI tools. What's new are a few things. One is with Cloudera Navigator, they have great technical metadata around security and lineage over HGFS, and that's a way to bolster our catalog to go even deeper into what's happening in the files before things get surfaced and higher for places where we have a deeper offering today. >> So it's almost a connector to them in a way, you kind of share data. >> That's exactly right, we've a lot of different connectors, this is one new one that we have. Another, go ahead. >> I was going to go ahead continue. >> I was just going to say another place that is exciting is data prep tools, so Trifacta and Paxata are both places where you can find and understand an alation and then begin to manipulate in those tools. We announced with Paxata yesterday, the ability to click to profile, so if you want to actually see what's in some raw compressed avro file, you can see that in one click. >> It's interesting, Paxata has really been almost lapping, Trifacta because they were the leader in my mind, but now you've got like a Nascar race going on between the two firms, because data wrangling is a huge issue. Data prep is where everyone is stuck right now, they just want to do the data science, it's interesting. >> They are both amazing companies and I'm happy to partner with both. And actually Trifacta and Alation have a lot of joint customers we're psyched to work with as well. I think what's interesting is that data prep, and this is beginning to happen with analyst definitions of that field. It isn't just preparing the data to be used, getting it cleaned and shaped, it's also preparing the humans to use the data giving them the confidence, the tools, the knowledge to know how to manipulate it. >> And it's great progress. So the question I wanted to ask is now the other big trend here is, I mean it's kind of a subtext in this show, it's not really front and center but we've been seeing it kind of emerge as a concept, we see in the cloud world, on premise vs cloud. On premise a lot of people bring in the dev ops model in, and saying I may move to the cloud for bursting and some native applications, but at the end of the day there is a lot of work going on on premise. A lot of companies are kind of cleaning house, retooling, replatforming, whatever you want to do resetting. They are kind of getting their house in order to do on prem cloud ops, meaning a business model of cloud operations on site. A lot of people doing that, that will impact the story, it's going to impact some of the server modeling, that's a hot trend. How do you guys deal with the on premise cloud dynamic? >> Totally, so we just want to do what's right for the customer, so we deploy both on prem and in the cloud and then from wherever the Alation server is it will point to usually a mix of sources, some that are in the cloud like vetshifter S3 often with Amazon today, and also sources that are on prem. I do think I'm seeing a trend more and more toward the cloud and we have people that are migrating from HGFS to S3 is one thing we hear a lot about it. Strata with sort of dupe interest. But I think what's happening is people are realizing as each Equifax in turn happens, that this old wild west model of oh you surround your bank with people on horseback and it's physically in one place. With data it isn't like that, most people are saying I'd rather have the A+ teams at Salesforce or Amazon or Google be responsible for my security, then the people I can get over in the midwest. >> And the Paxata guys have loved the term Data Democracy, because that is really democratization, making the data free but also having the governance thing. So tell me about the Data Lake governance, because I've never loved the term Data Lake, I think it's more of a data ocean, but now you see data lake, data lake, data lake. Are they just silos of data lakes happening now? Are people trying to connect them? That's key, so that's been a key trend here. How do you handle the governance across multiple data lakes? >> That's right so the key is to have that single source of reference, so that regardless of which lake or warehouse, or little siloed Sequel server somewhere, that you can search in a single portal and find that thing no matter where it is. >> John: Can you guys do that? >> We can do that, yeah, I think the metaphor for people who haven't seen it really is Google, if you think about it, you don't even know what physical server a webpage is hosted from. >> Data lakes should just be invisible >> Exactly. >> So your interfacing with multiple data lakes, that's a value proposition for you. >> That's right so it could be on prem or in the cloud, multi-cloud. >> Can you share an example of a customer that uses that and kind of how it's laid out? >> Absolutely, so one great example of an interesting data environment is eBay. They have the biggest teradata warehouse in the world. They also have I believe two huge data lakes, they have hive on top of that, and Presto is used to sort of virtualize it across a mixture of teradata, and hive and then direct Presto query It gets very complicated, and they have, they are a very data driven organization, so they have people who are product owners who are in jobs where data isn't in their job title and they know how to look at excel and look at numbers and make choices, but they aren't real data people. Alation provides that accessibility so that they can understand it. >> We used to call the Hadoop world the car show for the data world, where for a long time it was about the engine what was doing what, and then it became, what's the car, and now how's it drive. Seeing that same evolution now where all that stuff has to get done under the hood. >> Aaron: Exactly. >> But there are still people who care about that, right. They are the mechanics, they are the plumbers, whatever you want to call them, but then the data science are the guys really driving things and now end users potentially, and even applications bots or what nots. It seems to evolve, that's where we're kind of seeing the show change a little bit, and that's kind of where you see some of the AI things. I want to get your thoughts on how you or your guys are using AI, how you see AI, if it's AI at all if it's just machine learning as a baby step into AI, we all know what AI could be, but it's really just machine learning now. How do you guys use quote AI and how has it evolved? >> It's a really insightful question and a great metaphor that I love. If you think about it, it used to be how do you build the car, and now I can drive the car even though I couldn't build it or even fix it, and soon I don't even have to drive the car, the car will just drive me, all I have to know is where I want to go. That's sortof the progression that we see as well. There's a lot of talk about deep learning, all these different approaches, and it's super interesting and exciting. But I think even more interesting than the algorithms are the applications. And so for us it's like today how do we get that turn by turn directions where we say turn left at the light if you want to get there And eventually you know maybe the computer can do it for you The thing that is also interesting is to make these algorithms work no matter how good your algorithm is it's all based on the quality of your training data. >> John: Which is a historical data. Historical data in essence the more historical data you have you need that to train the data. >> Exactly right, and we call this behavior IO how do we look at all the prior human behavior to drive better behavior in the future. And I think the key for us is we don't want to have a bunch of unpaid >> John: You can actually get that URL behavioral IO. >> We should do it before it's too late (Both laugh) >> We're live right now, go register that Patrick. >> Yeah so the goal is we don't want to have a bunch of unpaid interns trying to manually attack things, that's error prone and that's slow. I look at things like Luis von Ahn over at CMU, he does a thing where as you're writing in a CAPTCHA to get an email account you're also helping Google recognize a hard to read address or a piece of text from books. >> John: If you shoot the arrow forward, you just take this kind of forward, you almost think augmented reality is a pretext to what we might see for what you're talking about and ultimately VR are you seeing some of the use cases for virtual reality be very enterprise oriented or even end consumer. I mean Tom Brady the best quarterback of all time, he uses virtual reality to play the offense virtually before every game, he's a power user, in pharma you see them using virtual reality to do data mining without being in the lab, so lab tests. So you're seeing augmentation coming in to this turn by turn direction analogy. >> It's exactly, I think it's the other half of it. So we use AI, we use techniques to get great data from people and then we do extra work watching their behavior to learn what's right. And to figure out if there are recommendations, but then you serve those recommendations, either it's Google glasses it appears right there in your field of view. We just have to figure out how do we make sure, that in a moment of you're making a dashboard, or you're making a choice that you have that information right on hand. >> So since you're a technical geek, and a lot of folks would love to talk about this, so I'll ask you a tough question cause this is something everyone is trying to chase for the holy grail. How do you get the right piece of data at the right place at the right time, given that you have all these legacy silos, latencies and network issues as well, so you've got a data warehouse, you've got stuff in cold storage, and I've got an app and I'm doing something, there could be any points of data in the world that could be in milliseconds potentially on my phone or in my device my internet of thing wearable. How do you make that happen? Because that's the struggle, at the same time keep all the compliance and all the overhead involved, is it more compute, is it an architectural challenge how do you view that because this is the big challenge of our time. >> Yeah again I actually think it's the human challenge more than the technology challenge. It is true that there is data all over the place kind of gathering dust, but again if you think about Google, billions of web pages, I only care about the one I'm about to use. So for us it's really about being in that moment of writing a query, building a chart, how do we say in that moment, hey you're using an out of date definition of profit. Or hey the database you chose to use, the one thing you chose out of the millions that is actually is broken and stale. And we have interventions to do that with our partners and through our own first party apps that actually change how decisions get made at companies. >> So to make that happen, if I imagine it, you'd have to need access to the data, and then write software that is contextually aware to then run, compute, in context to the user interaction. >> It's exactly right, back to the turn by turn directions concept you have to know both where you're trying to go and where you are. And so for us that can be the from where I'm writing a Sequel statement after join we can suggest the table most commonly joined with that, but also overlay onto that the fact that the most commonly joined table was deprecated by a data steward data curator. So that's the moment that we can change the behavior from bad to good. >> So a chief data officer out there, we've got to wrap up, but I wanted to ask one final question, There's a chief data officer out there they might be empowered or they might be just a CFO assistant that's managing compliance, either way, someone's going to be empowered in an organization to drive data science and data value forward because there is so much proof that data science works. From military to play you're seeing examples where being data driven actually has benefits. So everyone is trying to get there. How do you explain the vision of Alation to that prospect? Because they have so much to select from, there's so much noise, there's like, we call it the tool shed out there, there's like a zillion tools out there there's like a zillion platforms, some tools are trying to turn into something else, a hammer is trying to be a lawnmower. So they've got to be careful on who the select, so what's the vision of Alation to that chief data officer, or that person in charge of analytics to scale operational analytics. >> Absolutely so we say to the CDO we have a shared vision for this place where your company is making decisions based on data, instead of based on gut, or expensive consultants months too late. And the way we get there, the reason Alation adds value is, we're sort of the last tool you have to buy, because with this lake mentality, you've got your tool shed with all the tools, you've got your library with all the books, but they're just in a pile on the floor, if you had a tool that had everything organized, so you just said hey robot, I need an hammer and this size nail and this text book on this set of information and it could just come to you, and it would be correct and it would be quick, then you could actually get value out of all the expense you've already put in this infrastructure, that's especially true on the lake. >> And also tools describe the way the works done so in that model tools can be in the tool shed no one needs to know it's in there. >> Aaron: Exactly. >> You guys can help scale that. Well congratulations and just how far along are you guys in terms of number of employees, how many customers do you have? If you can share that, I don't know if that's confidential or what not >> Absolutely, so we're small but growing very fast planning to double in the next year, and in terms of customers, we've got 85 customers including some really big names. I mentioned eBay, Pfizer, Safeway Albertsons, Tesco, Meijer. >> And what are they saying to you guys, why are they buying, why are they happy? >> They share that same vision of a more data driven enterprise, where humans are empowered to find out, understand, and trust data to make more informed choices for the business, and that's why they come and come back. >> And that's the product roadmap, ethos, for you guys that's the guiding principle? >> Yeah the ultimate goal is to empower humans with information. >> Alright Aaron thanks for coming on the Cube. Aaron Kalb, co-founder head of product for Alation here in New York City for BigData NYC and also Strata Data I'm John Furrier thanks for watching. We'll be right back with more after this short break.