(upbeat music) >> Welcome back. We're here at Supercomputing 22 in Dallas. My name's Paul Gill, I'm your host. With me, Dave Nicholson, my co-host. And one thing that struck me about this conference arriving here, was the number of universities that are exhibiting here. I mean, big, big exhibits from universities. Never seen that at a conference before. And one of those universities is Indiana University. Our two guests, Dave Jent, who's the AVP of Networks at Indiana University, Aaron Neal, Deputy CIO at Indiana University. Welcome, thanks for joining us. >> Thank you for having us. >> Thank you. >> I've always thought that the CIO job at a university has got to be the toughest CIO job there is, because you're managing this sprawling network, people are doing all kinds of different things on it. You've got to secure it. You've got to make it performant. And it just seems to be a big challenge. Talk about the network at Indiana University and what you have done particularly since the pandemic, how that has affected the architecture of your network. And what you do to maintain the levels of performance and security that you need. >> On the network side one of the things we've done is, kept in close contact with what the incoming students are looking for. It's a different environment than it was then 10 years ago when a student would come, maybe they had a phone, maybe they had one laptop. Today they're coming with multiple phones, multiple laptops, gaming devices. And the expectation that they have to come on a campus and plug all that stuff in causes lots of problems for us, in managing just the security aspect of it, the capacity, the IP space required to manage six, seven devices per student when you have 35,000 students on campus, has always been a challenge. And keeping ahead of that knowing what students are going to come in with, has been interesting. During the pandemic the campus was closed for a bit of time. What we found was our biggest challenge was keeping up with the number of people who wanted to VPN to campus. We had to buy additional VPN licenses so they could do their work, authenticate to the network. We doubled, maybe even tripled our our VPN license count. And that has settled down now that we're back on campus. But again, they came back with a vengeance. More gaming devices, more things to be connected, and into an environment that was a couple years old, that we hadn't done much with. We had gone through a pretty good size network deployment of new hardware to try to get ready for them. And it's worked well, but it's always challenging to keep up with students. >> Aaron, I want to ask you about security because that really is one of your key areas of focus. And you're collaborating with counties, local municipalities, as well as other educational institutions. How's your security strategy evolving in light of some of the vulnerabilities of VPNs that became obvious during the pandemic, and this kind of perfusion of new devices that that Dave was talking about? >> Yeah, so one of the things that we we did several years ago was establish what we call OmniSOC, which is a shared security operations center in collaboration with other institutions as well as research centers across the United States and in Indiana. And really what that is, is we took the lessons that we've learned and the capabilities that we've had within the institution and looked to partner with those key institutions to bring that data in-house, utilize our staff such that we can look for security threats and share that information across the the other institutions so that we can give each of those areas a heads up and work with those institutions to address any kind of vulnerabilities that might be out there. One of the other things that you mentioned is, we're partnering with Purdue in the Indiana Office of Technology on a grant to actually work with municipalities, county governments, to really assess their posture as it relates to security in those areas. It's a great opportunity for us to work together as institutions as well as work with the state in general to increase our posture as it relates to security. >> Dave, what brings IU to Supercomputing 2022? >> We've been here for a long time. And I think one of the things that we're always interested in is, what's next? What's new? There's so many, there's network vendors, software vendors, hardware vendors, high performance computing suppliers. What is out there that we're interested in? IU runs a large Cray system in Indiana called Big Red 200. And with any system you procure it, you get it running, you operate it, and your next goal is to upgrade it. And what's out there that we might be interested? That I think why we come to IU. We also like to showcase what we do at IU. If you come by the booth you'll see the OmniSOC, there's some video on that. The GlobalNOC, which I manage, which supports a lot of the RNE institutions in the country. We talk about that. Being able to have a place for people to come and see us. If you stand by the booth long enough people come and find you, and want to talk about a project they have, or a collaboration they'd like to partner with. We had a guy come by a while ago wanting a job. Those are all good things having a big booth can do for you. >> Well, so on that subject, in each of your areas of expertise and your purview are you kind of interleaved with the academic side of things on campus? Do you include students? I mean, I would think it would be a great source of cheap labor for you at least. Or is there kind of a wall between what you guys are responsible for and what students? >> Absolutely we try to support faculty and students as much as we can. And just to go back a little bit on the OmniSOC discussion. One of the things that we provide is internships for each of the universities that we work with. They have to sponsor at least three students every year and make that financial commitment. We bring them on site for three weeks. They learn us alongside the other analysts, information security analysts and work in a real world environment and gain those skills to be able to go back to their institutions and do an additional work there. So it's a great program for us to work with students. I think the other thing that we do is we provide obviously the infrastructure that enable our faculty members to do the research that they need to do. Whether that's through Big Red 200, our Supercomputer or just kind of the everyday infrastructure that allows them to do what they need to do. We have an environment on premise called our Intelligent Infrastructure, that we provide managed access to hardware and storage resources in a way that we know it's secure and they can utilize that environment to do virtually anything that they need in a server environment. >> Dave, I want to get back to the GigaPOP, which you mentioned earlier you're the managing director of the Indiana GigaPOP. What exactly is it? >> Well, the GigaPOP and there are a number of GigaPOP around the country. It was really the aggregation facility for Indiana and all of the universities in Indiana to connect to outside resources. GigaPOP has connections to internet too, the commodity internet, Esnet, the Big Ten or the BTAA a network in Chicago. It's a way for all universities in Indiana to connect to a single source to allow them to connect nationally to research organizations. >> And what are the benefits of having this collaboration of university. >> If you could think of a researcher at Indiana wants to do something with a researcher in Wisconsin, they both connect to their research networks in Wisconsin and Indiana, and they have essentially direct connection. There's no commodity internet, there's no throttling of of capacity. Both networks and the interconnects because we use internet too, are essentially UNT throttled access for the researchers to do anything they need to do. It's secure, it's fast, easy to use, in fact, so easy they don't even know that they're using it. It just we manage the networks and organize the networks in a way configure them that's the path of least resistance and that's the path traffic will take. And that's nationally. There are lots of these that are interconnected in various ways. I do want to get back to the labor point, just for a moment. (laughs) Because... >> You're here to claim you're not violating any labor laws. Is that what you're going to be? >> I'm here to hopefully hire, get more people to be interested to coming to IU. >> Stop by the booth. >> It's a great place to work. >> Exactly. >> We hire lots of interns and in the network space hiring really experienced network engineers, really hard to do, hard to attract people. And these days when you can work from anywhere, you don't have to be any place to work for anybody. We try to attract as many students as we can. And really we're exposing 'em to an environment that exists in very few places. Tens of thousands of wireless access points, big fast networks, interconnections and national international networks. We support the Noah network which supports satellite systems and secure traffic. It really is a very unique experience and you can come to IU, spend lots of years there and never see the same thing twice. We think we have an environment that's really a good way for people to come out of college, graduate school, work for some number of years and hopefully stay at IU, but if not, leave and get a good job and talk well about IU. In fact, the wireless network today here at SC was installed and is managed by a person who manages our campus network wireless, James Dickerson. That's the kind of opportunity we can provide people at IU. >> Aaron, I'd like to ask, you hear a lot about everything moving to the cloud these days, but in the HPC world I don't think that move is happening as quickly as it is in some areas. In fact, there's a good argument some workloads should never move to the cloud. You're having to balance these decisions. Where are you on the thinking of what belongs in the data center and what belongs in the cloud? >> I think our approach has really been specific to what the needs are. As an institution, we've not pushed all our chips in on the cloud, whether it be for high performance computing or otherwise. It's really looking at what the specific need is and addressing it with the proper solution. We made an investment several years ago in a data center internally, and we're leveraging that through the intelligent infrastructure that I spoke about. But really it's addressing what the specific need is and finding the specific solution, rather than going all in in one direction or another. I dunno if Jet Stream is something that you would like to bring up as well. >> By having our own data center and having our own facilities we're able to compete for NSF grants and work on projects that provide shared resources for the research community. Just dream is a project that does that. Without a data center and without the ability to work on large projects, we don't have any of that. If you don't have that then you're dependent on someone else. We like to say that, what we are proud of is the people come to IU and ask us if they can partner on our projects. Without a data center and those resources we are the ones who have to go out and say can we partner on your project? We'd like to be the leaders of that in that space. >> I wanted to kind of double click on something you mentioned. Couple of things. Historically IU has been I'm sure closely associated with Chicago. You think of what are students thinking of doing when they graduate? Maybe they're going to go home, but the sort of center of gravity it's like Chicago. You mentioned talking about, especially post pandemic, the idea that you can live anywhere. Not everybody wants to live in Manhattan or Santa Clara. And of course, technology over decades has given us the ability to do things remotely and IU is plugged into the globe, doesn't matter where you are. But have you seen either during or post pandemic 'cause we're really in the early stages of this. Are you seeing that? Are you seeing people say, Hey, thinking about their family, where do I want to live? Where do I want to raise my family? I'm in academia and no, I don't want to live in Manhattan. Hey, we can go to IU and we're plugged into the globe. And then students in California we see this, there's some schools on the central coast where people loved living there when they were in college but there was no economic opportunity there. Are you seeing a shift, are basically houses in Bloomington becoming unaffordable because people are saying, you know what, I'm going to stay here. What does that look like? >> I mean, for our group there are a lot of people who do work from home, have chosen to stay in Bloomington. We have had some people who for various reasons want to leave. We want to retain them, so we allow them to work remotely. And that has turned into a tool for recruiting. The kid that graduates from Caltech. Doesn't want to stay in Caltech in California, we have an opportunity now he can move to wherever between here and there and we can hire him do work. We love to have people come to Indiana. We think it is a unique experience, Bloomington, Indianapolis are great places. But I think the reality is, we're not going to get everybody to come live, be a Hoosier, how do we get them to come and work at IU? In some ways disappointing when we don't have buildings full of people, but 40 paying Zoom or teams window, not kind the same thing. But I think this is what we're going to have to figure out, how do we make this kind of environment work. >> Last question here, give you a chance to put in a plug for Indiana University. For those those data scientists those researchers who may be open to working somewhere else, why would they come to Indiana University? What's different about what you do from what every other academic institution does, Aaron? >> Yeah, I think a lot of what we just talked about today in terms of from a network's perspective, that were plugged in globally. I think if you look beyond the networks I think there are tremendous opportunities for folks to come to Bloomington and experience some bleeding edge technology and to work with some very talented people. I've been amazed, I've been at IU for 20 years and as I look at our peers across higher ed, well, I don't want to say they're not doing as well I do want brag at how well we're doing in terms of organizationally addressing things like security in a centralized way that really puts us in a better position. We're just doing a lot of things that I think some of our peers are catching up to and have been catching up to over the last 10, 12 years. >> And I think to sure scale of IU goes unnoticed at times. IU has the largest medical school in the country. One of the largest nursing schools in the country. And people just kind of overlook some of that. Maybe we need to do a better job of talking about it. But for those who are aware there are a lot of opportunities in life sciences, healthcare, the social sciences. IU has the largest logistics program in the world. We teach more languages than anybody else in the world. The varying kinds of things you can get involved with at IU including networks, I think pretty unparalleled. >> Well, making the case for high performance computing in the Hoosier State. Aaron, Dave, thanks very much for joining you making a great case. >> Thank you. >> Thank you. >> We'll be back right after this short message. This is theCUBE. (upbeat music)

(upbeat music) >> Welcome back to Boston. The CUBE's coverage of AWS Re-inforce 2022. This is our second live Re-inforce. We did two in the middle that were all digital. Aaron Brown is here as US AWS cyber leader for Deloitte and Ryan Orsi the cloud foundation leader for partners for Amazon Web Services. Jen, welcome to The CUBE. >> Thanks for having us. >> Thanks. >> Nice to see you. Tell us about the story of Deloitte in cyber and then we'll get it to Deloitte cyber on AWS, or maybe even start there. >> Yeah, sure. I mean, obviously Deloitte, one of the largest cyber consultancies in the world, we've been working with AWS for a very long time. 2013, I was involved with, you know, the first Alliance agreement with them. And then we've been in cloud managed services about five years delivering workloads for clients. We have over 200 clients on that platform and then about a year and a half ago or so, the MSSP program came and it made a ton of sense to us, right? To really level the playing field and gave us a chance to really come out and demonstrate, you know, our capability around MSSP. >> The MSSP program, I saw a slide yesterday in keynote and in the analyst program was, you know, there's technology partners, there's MSSP partners. Explain the MSSP partner. >> Sure, sure. So at the Database Partner Network, we break it down. The program is called the level one MSSP Competency Program. And it is for both those companies that are sort of more of a software company with a managed service and those that are more of a pure service company, it's for both, but it's the general concept, it hosts the community of partners like Deloitte with a concentrated talent pool around 24 by 7 monitoring and response of AWS security events. >> So what is Deloitte? Deloitte's not a pure software play. It's not a pure services play anymore. It's sort of a mixture. >> Yeah, you know, asset enabled services, right? It's the way that we look at it. So, yeah, we're definitely not trying to compete with software companies out there, but we do have assets, right? So we do everything as infrastructure as code and that allows us to deploy our solutions into client environments really quickly. So where you might spend months on third party tool integrations, we leverage all native AWS tools in our standard offering and we can deploy into a client and get those services up and running in a couple of weeks. >> So you sell your software as an integrated service, is that correct? You don't- >> It's service, it's really is service. We sell a metered service. >> You don't sell your software separately? >> No. >> I should say it differently. You include your software as part of the service, is that right? >> Yeah, it is. But actually there's another element. There are obviously some clients who don't want to be in a managed service in perpetuity. And so those same assets that I talked about that we use for MSSP, you know, for the right clients, we don't just give away everything to anybody but for the right clients, for the right engagement, we will work with clients to help them build the capability that they need to run it themselves. And our solution is built in a way where they can do that. Right? We have a base component and a variable component to the solution and we will impart those assets to a client, you know, if the situation is right. >> Okay. So you'll actually transfer the software, but would you charge for that? >> Yeah, certainly, but there's obviously a big service component that goes into it. Right? >> And that's really where your expertise is. >> Yeah, we don't have like a standard, you know, list price but we'll work with clients to basically help them build out that capability because frankly the the market moves so fast that you need a constant capability and engine to update that solution. It's not something that, you know, you're going to sell and someone's just going to use that out of the box for the next five years. >> But a lot of the value that seems that Deloitte brings is you don't run from customization. You welcome that. You, you know, if a client says, hey, I need this special and that special, or whatever it is you'll go attack. You have the staff, the talent to attack that problem. And you use software in areas where you can have repeatability and it helps you scale and be more productive. Is that a fair way to think about it? >> Yeah, that's right. I mean, I guess one of the phrases that we use is we like big hairy problems, right? That's sort of our sweet spot. The, you know, the very simple, hey, I need a couple of guys to do a couple of things, typically, we're not the right firm for that. So, yes, we use the assets cause we realize like, hey, you know, out of everything that needs to be done, there's a significant portion of this that everybody needs more or less the same way. And then we build that, we build the automation to get it in and then we have that variable component working with clients to say, hey, let's make this work in your environment. We use a combination of AWS Native services, but then, you know, some clients have investments in third party tools and we can work with that. >> So it's a perfect match for AWS cause you guys are all about providing tools for builders and here's some primitives, some APIs and Go, we don't want that highly customized snowflake for every single client. >> Exactly. I mean, that's what I feel like the partnership with Deloitte is really bringing to the table for everybody and our mutual customers and builders out there that we both work with is again, they don't run from complexity or customization that security can be complex. It can be hard, Deloitte's helping making it much easier. The AWS partner network is helping kind of bring the ecosystem together and of software service, architectures that AWS recommend for like a security best practice around what to monitor, how to respond, what kind of enriched data should be added to that security finding and kind of pushing that out through our partnerships with it such as Deloitte. >> One of the things that, I mean, certainly big takeaway from this event, the security tracks that reinvent, previous Re-inforce events is AWS imparting, educating its customers on best practice and how tos and things that they should be thinking about, you know, do this, don't do that. In 2019, it was a lot about, hey guys, there's this shared responsibility model and kind of explaining that, we're way, way beyond that now, should we think about Deloitte sort of as an extension of that best practice AWS expertise that can be applied at your clients? I'll go to Deloitte because I don't have the talent to deal with that. I mean, I got talented people, but I just don't have enough of them. >> Exactly. Yeah. Yeah. And that's really, you know, our offerings tend to be comprehensive across all the domains. And like I said, the full life cycle of security operations all the way from, you know, identify the issue to resolve it and recover from it. And, you know, when we look at the shared responsibility model, you know, we like to say, hey, we will take you really far up that stack, that customer responsibility area, you know, for our service, we cover a significant portion of that landscape on our client's behalf cause, you know, what do they care about? Deploying workloads, getting the application running, right? Security is just another one of those important, necessary things, but it just sort of standing between you and the business value of your workload. >> And your ideal target customer would be a large medium up to a large enterprise or is all exclusively large or? >> Definitely not exclusively large. You know, the fact that we have all the automation that we do, we have a significant portion of our security operations folks are offshore allows us to be really competitive. And so we're able to serve clients that maybe, you know, in years past wouldn't have been what you'd think of as traditional. So like clients leveraging the marketplace, you know, we're able to serve that market segment. >> So billion dollar up kind of revenue? Odes that sound about right? >> Yeah. Even south of that a bit. >> Okay. So maybe half a billion or 500 million up. >> Yeah. >> Okay. So thinking about that ideal sort of profile, if you don't know, you don't know, I'm going to ask you to guess. >> Yeah. >> What percent of those target companies, enterprises, have a SOC? Is it 100%, 50%, you know, or are you- >> 75, 75% most so. >> Okay. So let's say 3/4. >> Yeah. >> So you compliment the SOC, right? You're not the SOC, but you may be in some cases? >> Depending, now we're talking about it's a function of what their IT enterprise landscape looks like. If they're 100% AWS, yeah. If you're born in the cloud startup and, you know, you don't do anything else and we have, you know, we have a few of those. Right. And they want to give us everything. They're like, you know, our security guys just going to kind of understand what you guys are doing and feel good about it. Yeah. We do that. But for the most, there is an existing SOC. Right. And so what we do is we leverage, you know, an ITSM software to e-bond with our clients service management functions so that when we're generating tickets, they have full visibility to what's going on. We're still resolving things on their behalf, we need to communicate with some clients, right? Cause a lot of security issues that need to get resolved require engagement with the asset owner. So we're not just a black box. So we do have to talk to folks on the ground at the client to resolve issues. >> And that's actually one thing that really impressed me to getting to know Aaron and his team more and more throughout this journey together in the partnership is they're not throwing alerts over the fence to the customers SOC team saying, well, here's some recommended remediation steps, they're actually rolling up their sleeves and doing some remediation themselves and informing the customer. This was taken care of for you. I think that's really unique. >> Yeah. In addition to, you know, our solution obviously has a bunch of auto-remediations, you know, that we do as part of the solution. >> So what's the engagement like? What's the conversation like when people come to you? Say I have a problem, it's blank, right? What are the typical blank- >> You know, a lot of it has been organizations where there's either a business unit that has kind of maybe off run and doing their own thing. And, you know, it's only sort of come to light with the compliance and security organization inside the client that like, hey, these guys maybe need some help. And boy, we're really strapped. We don't have the people cause talent's so tight to go help these guys and make them get it right. We're going to go ahead and keep them kind of off to the side. And you know, we'll do this managed service to help get that addressed. And then another typical scenario is when companies are acquired. So, you know, organization buys a company and they've got a preexisting. Again, they look under the covers and they're like, oh, these guys really need some help because of the way that we deploy everything as infrastructure as code really very quickly, it's a great way to just kind of get it sorted. It's a metered service. So it's not some massive investment that they have to make. We could just get it sorted out until maybe they get a chance to process and actually onboard that new entity into their enterprise structure. So as part of the MSSP program within AWS, you got to be really good at understanding how to utilize the AWS portfolio of cyber security services natively. So you do that, does that check the box on everything you need or do clients typically say, no, no, you got to integrate with all this other mess that I have there. Can you sweep that mess aside and say, hey, I can do this all in the cloud or what's that dynamic like? >> The answer is, yes, both. Right? So, you know, typically clients will have significant investments in existing third party tools and then either politically because of the investment or from a practical standpoint it makes sense to integrate those. Now that does slow down, you know, the deployment and the customization a bit, but, you know, and a lot of times that makes sense for the client. >> Well, it gets hairy. Like you said, you love these kind of hairy problems, right? >> Yeah, that's right. >> You run towards that. >> That's right. We run towards fire >> And, Ryan, your focus on partners is all partners or is it really the MSSPs or? >> All partners, all kinds of partners in the security space, right? >> Right, right. Yeah. Of course. >> Software companies, professional services, managed services. And we're focused on trying to make the security easier for both of our mutual customers here. Right? So that what you mentioned about best practices and, you know, how do you tell what best practices are per AWS service or third party software that's operating in an AWS environment? That's part of what our team does is we create these partner programs. There's a very detailed, very prescriptive technical checklist that out internal security experts are going through with Deloitte folks, for example, as a part of their membership and the level one MSSP program to make sure that, right? Those best practices which could be fresh off the AWS documentation truck are built into their services. And the reason those best practices exist is for a for a good reason. They're built, tried and tested, you know, in our own environments before they reach the documentation website. But all of that is incorporated into that whole kind of validated checklist that we do together. So it's a great way to make sure that operations from partners like Deloitte, software delivered, customization delivered, aligns with what we're able to see from just our Amazon culture of being so customer obsessed and really listening to all of those very specific challenges they might have that the customer will have at different points in their cloud journey. Those challenges are baked directly into key technical requirement criteria that Deloitte's teamed up with us to go achieve. >> What are you seeing at the macro, Aaron? When we talked to practitioners where we'll survey, we have a survey partner called ETR and they'll do spending surveys coming into the year of CIOs and IT buyers, we're expecting 8%, eight to 8 1/2% budget growth, post Ukraine, inflation, Fed tightening, you know, the tech lash, all that. It's dialed down a bit, it's still pretty robust it's 6% and security still remains the number one priority. And we've seen a little bit of momentum deceleration even in security spend across the board, but not anything, you know, tragic. Are you seeing the same or are you seeing security budgets kind of where they were expected to be at the beginning of the year? >> Yeah, you know, I haven't seen it decline. I mean, I think the fact of the matter is for all the things that we talked about before, right? Basically the skill shortages and just the coordination with other cloud programs, there's a tremendous backlog of stuff that needs to be done. And, you know, enterprises have more appreciation now for the need for all, you know, all the various, you know, ransomware things that have happened and others that, hey, they need to get a handle on the security and their environment. And so I think a lot of what's been going on in the last year, the reason it hasn't been faster, hasn't been for a lack of appetite. It's just been a lack of skills and process to do it. >> Has the business case changed? And the variables maybe the same, but it used to be, hey, if you don't do this, you're exposed. Okay. Here's the fear of getting, you know, infiltrated and then it's going to became if you want to quantify it, it's like, okay, what's the expected loss with, and without, you know, the kind of think of insurance terms. Is the business case shifting with digital toward this is a fundamental component of monetization in order to be able to monetize, you have to ensure this level security. Are we there yet? >> Yeah, I think so. I don't think anyone's arguing whether it's, you know, needed or not. Right. So now it's a question of, hey, and I think CJ Moses had a good slide in the opening yesterday where he was saying, you know, was it, make the secure path, the path of least resistance. Right? And so that's a big part of, you know, how we deliver our solution. We really want to make it easy for the enterprise to absorb the security services that we have. Right? And that's really critical. I think that's where the focus is, is make it easier to do security because the value comes right along with it. >> All right. I'll give you each the final word, Ryan, you go first then Aaron kind of put a bumper sticker on Re-inforce 2022. >> It's not slowing down. It's only picking up in terms of innovation, software tools, operational processes, and some of the unique ways that all these tools are tied together. Third party, Native AWS, consulting, the way these services come together, it's only accelerating. It's been pretty exciting to see some of the innovation here this time at this Re-inforce. >> Right, Aaron, what do you say? >> Yeah, I would agree. I mean, just the breadth of capabilities, the new announcements by AWS of the capabilities in their solution stack. I mean, for me, you know, I just kind of wonder like when does it narrow or when does it settle down and I know that that's not now. >> Keep waiting. >> Yeah. >> But, yeah, I think, you know, we will continue to see you know, just rapid acceleration and new features and services that... >> I often say the next decade at cloud ain't going to to be like the last. So gentlemen, thanks for coming on The CUBE. It's great to see you. >> Thanks for having us. Thank you everything. >> All right, thank you for watching. Keep it right there. This is Dave Vellante for The CUBE. We'll be back right after this short break from Boston AWS Re-inforce 2022. (soft music)

>>Mm. Joining me is Aaron Suzuki, founder and CEO of Prowess. >>Aaron. Welcome. Thank you. Thanks so much for having me. >>Absolutely. Thanks for joining us. So let's dive right in. Tell us about prowess. >>Progress has been around for quite a while. We've been serving the technology industry from the very beginning, almost 20 years. We've always been able to bridge the gap between the story of the product and what it actually does. And a lot of times, there's a pretty fundamental disconnect between what engineering says and what marketing wants to claim. And so this is sort of how we got down this road of getting into testing and validation of products such as we do today quite quite extensively. And >>that's really what we're focusing on right now is this idea of your independence as a lab. And in this particular case, uh, it's a series of tests that you've done for, uh, you know, using Dell hardware combined with Broadcom cards. So talk a little more about that. About that the concept of independence and what that means. >>Yeah. You know, it's important to us that we stay vendor, agnostic, platform agnostic. Um, and there are a lot of things happening concurrently in the industry. A lot of people want to get a lot of work done really fast, and most customers are not sort of vendor exclusive. In fact, we're not sure we know of any. We always try to keep this objective point of view. That is to say that we don't allow our customers to buy results when we're doing quantitative testing. We really are out there trying to come up with a story or a narrative, and that really seemed to be The missing link in all of this is that there are the quantitative houses that do traditional benchmark testing on one side and then system integrators and kind of on the other extreme agencies. That would really do the narrative and the system integrator side build out a solution, but they wouldn't be able to tell you how it would perform. And so reconciling those two things really became challenging. So having a source that would be able to give you that insight that goes beyond just transactions, um, you know, per whatever unit of time and finding some of these metrics in between that we're more relevant to people's jobs. Where was really the inspiration for creating this unique practise that we call prowess? Labs? >>So, Erin, when I think about performance testing, uh, it's very easy to think of it from the perspective that it's a bunch of hardware slapped together in Iraq. You get some engineers, scientists to run some tests. Why prowess? What? What do you specifically bring to the table? That's meaningful? >>Performance testing is usually done in one of two ways, predominantly one way. It's a very academic approach, which says this specific benchmark test run this specific way gives us X. Another approach is more narrative in nature and more demonstrative. And there's this huge gap in between, and that's really what prowess labs exist to fulfil. >>So, Erin, give us an idea of the scale of prowess. How many of these projects have you worked on? How many how many customers have you worked with over >>over time? Um, we do this work with most of the leading global hardware and software manufacturers and a select number of emerging providers as well. So for us, you know, year to year dozens of projects of varying scope and scale. Various projects also running kind of programmatic form where we're kind of iterating constantly throughout the year. Um, so it's It's really a lot of fun for our team members to to do this. And some of them have been doing it for 10 or 12 years in continuity. >>Erin, Thanks for joining us to talk about practise today. >>My pleasure. Thanks for having me. Mhm. Yeah.

>>Welcome back to the cubes, continuous coverage of AWS reinvent 2021. I'm Lisa Martin. We are winning one of the industry's most important in hybrid tech events this year with AWS and its enormous ecosystem of partners to life sets we have going on right now. There's a dueling set right across from me, two remote studios over 100 guests on the program. We'll be digging into really the next decade of cloud innovation. I'm pleased to welcome two guests that sit next to me here. We've got Aaron Arnold Santa's partner at BCG gamma and a diesel associate director of data science at BCG gamma guys. Welcome to the program. Thanks for having us. I D let's go ahead and start with you. Give us the low down what's going on at BCG gamma. >>We are focused on building responsible, sustainable, and efficient AI at scale to solve pressing business problems. >>Good. We're going to dig into that more. There was a lot of talk about AI this morning during the keynote yesterday as well. And you know, one of the things Aaron that we talked about the last day and a half is that every company, these days has to be a data company, but the volume of data is so great that we've got to have AI to be able to help all the humans process it, find all of the nuggets that are buried within these volumes of data for companies to be competitive. You talk about a sustainable efficient let's go ahead and talk about what do you mean by efficient AI? It sounds great, but help unpack what that actually means. And, and how does an organization in any industry actually achieving? >>Yeah. So when we talk about efficient AI, we're really talking about resilience, scale and adoption. So we all know that the environment in which AI tools and systems are deployed change and update very frequently, and those changes and updates can lead to errors, downtime, which erode user trust. And so when you're designing your AI, it's really critical to build it right and, and ensure it's resilient to those types of changes in the operational environment. And that really means designing it upfront to adhere to company standards around documentation, um, testing bias as well as approved model architecture. So, so that piece is really critical. The other piece about efficient AI is we're really talking about using better code structure to ensure that and enable that teams can search learn, um, and really clone AI IP to bring AI at scale across company silos. So what efficient AI does is it ensures that companies can go from proof of concept and exploration to deploying AI at scale. The final piece is really about solving the right business problems quickly in a way that ensures that users and customers will adopt and actually use the tool and capability >>That adoption there is absolutely critical. And >>You know, when we, when we're talking about AI, most of the time we're talking about three components and we call it like the ten twenty, seventy rule, 10% of the change is really about the better AI algorithms that are coming out. 20% is a better architecture, the technology, all of those components, but 70% of it is really about how are we influencing our business partners to make better decisions? How are we making sure AI is built right into the operational decision flow? And that's really, when we start talking about better AI, we move it away from kind of our pet project, buzzword bingo, into decision operational flows, you know, and, and there's, there's a journey there, there's a journey that we all are on. You see the evolution of AI right now. And I th and I liken it a lot to, um, myself when I'm, I'm a big football fan, right. And I've fantasy football is like my passion. I see. And when I look at the decision-makings, I've made 10 years ago versus now, now I actually have my own models. I'm running against it. I'm, I'm very much into the details of what is the data telling me, but, um, it's not until I bring that together with my decision making process, that really makes it so that I have bragging rights on Sundays. >>I wouldn't want to compete against Aaron. I mean, you know, I've got a lot of friends that do fantasy football, but I don't think they're taking, they're actually doing data-driven approaches as you are. One of the things I'm glad that you talked about the 10, 20, 70 formula for in dividing investments in AI. One of the things that really surprised me, and I'm looking at my notes here, because I was writing this down was that you said 10% AI and machine learning algorithms, 20% software and technology infrastructure, 70% though is also change management. That is hard, especially the speed with which every industry is operating today. What we've seen in the last 22 months, we've seen a massive acceleration to the cloud, every business pivoting, many times where our customers, in terms of understanding the challenges that they can solve with AI, given the fact that we're still in such a dynamic global environment, do what are you seeing? >>So I think it's actually quite, bi-modal some companies, including the public sector are really leaning in and exploring all the different applications and all the different solutions. Unfortunately, if they're not emphasizing that 70% on change management and the culture change and user adoption, those are substantial, but you don't get the return on the investment. Right. On the other hand, the other part of that bi-modal distribution is there are folks who are still really reluctant because they have made investments and it hasn't right. Brought about the change that they were hoping for. And so I think it's really critical to bring that holistic approach to bringing AI and advanced analytic tools to really change the way, you know, a company's attacking its problems and bringing solutions to its users and customers. >>Yeah. I like it a lot to us as us, as adults have when we teach our kids about math, right? Like less of my time with my own kids is focused on teaching them, the principles, the, and all those things, but it's more teaching them to be comfortable. Why are they learning math? What are they doing? How is that going to prepare them to be more competitive and, uh, later on in life. And so, and then the same thing's happening in this evolution in AI, right? There is this big tech and AI transformations that are happening. But the questions we need to ask ourselves within is are we taking the time to make sure our companies and our people are on the journey with us and that they understand that this is going to be better for them and give them a competitive advantage. >>That's critical. We know we've talked a lot in Alaska. We talk a lot about every show about people, process technologies and people as part of that. But I've definitely seen more of a focus. I think the last two and a half days of the people emphasis going, we have to have, we have to upskill our people. We have to train our people. We have to make sure that they're understand how this technology can partner with them and enable them rather than take things away. So it's nice to hear you talking about the big focus there being on the people that is because without that, then a deed to your point, a lot of those projects aren't successful >>And not only, I think the other piece there in terms of bringing the user along for the journey is you don't want them to feel like this is just another tool, right? Another part of their, in addition to their workflow, right? You want to take the burden away. You want it to really, um, not add, but to, to their, to their list of, of daily tasks, but subtract and make it easier. Right. And I think that that's really critical for a lot of companies as well. >>I think along with what you're talking about, we have to teach people to be responsible. So it's, it's one thing to do the job better, but it's another thing to be responsible because in today's world, we have to think about our responsibilities back to our communities, to our consumers, to our shareholders and into ultimately to the environment itself. And so responsive as we are thinking about AI, we need to think differently too, because let's face it. Data is fuel and we can accidentally make the wrong decisions for the globe by making the right decisions for stakeholders. We have to do a better job of understanding the why we're doing what we're doing, what we're doing, and not only the, the intended consequences of our decisions, but also the unintended consequences. And then we need to be responsible in the ways that we're using AI and that we're transparent in our use thereof. >>Yeah, Aaron, I think that's incredibly critical. I think responsible AI, um, has to be at the heart of, of AI transformation. And one of the interesting things that we have found is that organizations perceive their responsible AI maturity to be substantially higher than it actually is. Right. And less than 50% of organizations that have, you know, a fully implemented AI at scale, do not have a responsible AI, um, capability. And so at BCG, we've been working quite hard to integrate our gamma responsible AI program into the big AI transformations, because it's so critical. It's so absolutely important. And, and really that there's a lot of facets to that. But one of that, one of the critical ones is an ensures the goals and the outcomes of the AI systems are fair and unbiased and explainable, which is so critical. Um, I think it also ensures best that we follow best practices for data governance to protect user privacy, which I think is another critical, um, piece here, as well as minimizing any negative social or environmental impact, which again, I it's, it's just gotta be at the forefront of AI development. What about, >>And I think that there's a tech part to that too. So like one thing that we're working on called a gamma facet is really, you know, for the longest time in this AI transformation, AI was kind of a black box and it's kind of mystical, but we, we optimize our results. The transformation, when we talk about better, AI is, uh, the decision maker is in the center and knows the outcome. And so we make it a clear box. And so they're really, we're working a lot on, you know, the most common Python packages, uh, to make them more clear too, so that the business user and the data scientist understands the decisions that they're making and how it will impact the company and longer term society. >>What about the sustainability front? I mean, it's clear that I can understand why you have the 10 20, 70 approach that, that 70% is really important. There are companies that think they're much farther advanced in terms of responsible use of responsible AI responsibly than they really are. Um, but you know, we talk about sustainability all the time. It's a buzzword, but it's also something that's incredibly important to you to companies like AWS. I imagined a companies like yourself, where does, what does sustainable AI look like and how to organizations implemented along with responsible AI efficient AI? >>Yeah, I think it's the question in some ways right now, given everything that's happening around the world. And so AI for sustainability is, is really critical. I think we all have a part to play in this fight, um, to ensure our, our global environment. So I think we need to use the same AI expertise, the same AI technology that we bring to maximize revenue and minimize cost, um, to, to minimizing a company's footprint. Long-term I think that's really critical. One of the things we've seen is that 85% of companies want to reduce their emissions, but less than 10% of them know how to accurately measure right. Their footprint. And so we've been focusing on AI for sustainability across a couple of different pillars. The first is measuring the current impact from operations. The second is data mining, um, for optimal decisions to reduce that footprint. And the third is scenarios to plan better strategies to alter our impact. >>Excellent. Well, there's so much work to be done, guys. Thank you for joining me talking about what BCG is doing for responsible, efficient, ethical, and sustainable AI, a lot of opportunities. I'm sure for you guys with AWS and your list of clients, but we thank you for taking the time out to talk with us this morning. So much. I write for my guests. I'm Lisa Martin. You're watching the cube, the global leader in live tech coverage.

>>Welcome back everyone to Dell Technologies World 2021 the virtual version. You're watching the cubes continuing coverage of the event and we're gonna talk about the Edge, the transformation of telco in the future of our expanding tech universe. With me is Aaron Jason, who's the vice president? Edge and Telkom marketing at Dell Technologies erin great to see you. I love this topic. >>Absolutely. It's it's pretty popular these days. I'm glad to be here with you. Thanks. >>It is popular, you know, cloud was kind of the shiny new toy last decade and it's still growing at double digits but it's kind of mainstream and now the Edge is all the rage. What's the best way to think about? What is the Edge? How do you define that? >>Yeah, you know, that's probably one of the most common questions I get is we start really doubling down on what we're doing it in the Edge world today. Um you know, I tried to basically not overcomplicated too much, you know, last year we really tried to to talk about it as being where you're the physical world, in the virtual world, connect. Um but you know, really it's more about what customers are looking to do with that technology. And so what we're really thinking about it today is the edges really where customers data is being used near point of generation to really define and build the essential value for that customer and that essential value is gonna be different in each vertical in each industry. Right? So in manufacturing, that essential value is created in the factory and retail, it's going to be, you know, at point of sale, whether that's in a store or on your device, in a virtual interaction, um in health care, it's going to be the point of care, Right? So it's gonna be the ambulance or the emergency room or the radiology lab. and of course in farming that essential values created in the field itself. So um, you know, for for many customers, it's really trying to figure out, you know, how do they take technology closer to the point of that value creation to be able to drive new new capabilities for the business, whether it's for what they're trying to accomplish or what they're trying to do in helping their customers. So really that's how we're thinking about the edge today. It's where that value generation occurs for a company. And how do we take technology to that point of generation to deliver value for them? >>Yeah, I like that. I mean to me the edge, I know what it's not, I know the edges, not a mega data center, but but everything else could be the edge. I mean, it's it's to me it's the place that's the most logical, the most logical place to process the data. So as you say, it could be a factory, it could be a hospital, it could be a retail store, it could be, could be a race track, it could be a farm, I mean virtually anything. So the edges, it's always been here, but it's changing. I mean most of the edge data has historically been analog. Everything now is getting instrumented. What are the factors that you think will make this, this industry's vision of the edge real in your opinion? >>Yeah. You know, it's it's really bringing together a handful of technologies that have really started to mature after over the last decade or so. Um the ones that have been around for a little bit, things like IOT have been emerging in the last several years. Um even Ai and machine learning many of those algorithms have been around for decades, but we've only recently been able to bring the compute power required to do that in edge environments in the last decade or so. Um it's so really the two key sort of killer technologies that have matured in the last couple of years is really the mic realization of computing. So being able to put compute almost anywhere on the planet and then the emergence of five G networking, giving us the ability to provide very high performance, low latency and high bandwidth environments to connect all those things together and get the data to those analytics environments. From that computer perspective. I mean, I still like to talk about moore's law as an example of that that ever marched that's been going on for, you know, half a century or more now is continuing to push forward um at a rate that is that that that that just really hasn't slowed down for the most part, you know, the example that I use with people, as, you know, you know, I still remember when I got my first calculator watch as a kid, you know, that Casio calculator watch that so many of us had, And my dad told me the story when he gave it to me, he's like, Hey, look, this has the same amount of compute power as the landing module on the moon, and I didn't know it at the time, but that was my first sort of entry and education around what Moore's law provided. And it's not so much speed. I mean, people think about that as it doubles in speed every 18 months, but it's really more about the density of compute that happens that moore's law drought, pushes along, so I can now squish more and more compute power into a smudge smaller location and I can now take that performance out to the edge in a way that I haven't been able to do before. I mean I think about my history, I joined E M C, that was acquired by Dell Technologies a couple years back. I joined that back in the late nineties when the biggest baddest storage array on the planet was one whole terabyte in size. And now I can fit that in the palm of my hand. In fact, when I walk around, you know, when I used to walk around with my, with my back, my laptop and go into offices, um you know, if I had my laptop and my tablet and my my my smartwatch, I had 12 to 16 cores on me and a couple of terabytes of capacity all connected with the equivalent of tens of T ones. Right? So what was once a small or or a mid sized data center just in the last decade or so? We now all walk around a small data centers and the power that that compute now brings to the edge allows us to take analytics that was really once done in data centers. I may have captured it at the edge, but I had to move it into a data lake. I had to stage it and analyze it. It was more of a historical way of looking at data. Now I can put compute right next to the point of data generation and give insight instantaneously as data is being generated. And that's opening up whole new ways that industries can drive new value for them and for their customers. And that's really what's exciting about it is this combination of these technologies that are all sort of maturing and coming together at the same time. Um, and there's just so much doing, it happened that space and devils really, really excited to be part of bringing that into these environments for our customers. >>I'm gonna give you a stat that a lot of people, I don't, I don't think realize, uh, you talked about moore's law and you're absolutely right. It's really, you know, technically moore's law is about the density, right? But the outcome of being able to do that is performance. And if you do the math, you know, moore's law doubling performance every two years, roughly, The math on that is that means 44 improvement per year in performance. Everybody talks about how moore's laws is dead. It's not, it's just changing. Here's the, here's the stat. If you take a system on a chip, take like for instance apples a 14 and go back five years from 2015 to 2021. If you add up the performance of the CPU the combinatorial factors of the CPU gpu and in the N. P. U. The neural processing unit, just those three, The growth rate has been 118 a year vs 44%. So it's actually accelerating and that doesn't include the accelerators and the DSPS and all the other alternative processors. So, and to your point and by the way that a 14 shipping cost Apple 50 bucks. So and and that fits in the palm of your hand to the point that you were just making So imagine that processing power at the edge most of of of of of ai today is modeling, let's say in the cloud, the vast majority is going to be a i influencing at the edge. So you are right on on that point. >>Yeah, there's no question about it. So, to your point, I mean, moore's law is just of course CPU itself. All right. And it comes out to roughly, on average, it's about 10 x every five years. 100 X every 10 years, 1000 X every 15 years. I mean, it's incredible how much power you can put in a small footprint today. And then if you factor in the accelerators and everything else um, it's actually if anything that innovation is going faster and faster and to your point, um you know, the while the modeling is still going to typically happen in data centers as you pull together lots of different data sets to be able to analyze and create new models. But those models are getting pushed right out to the edge on these compute devices literally feet away at times from the point of data generation to be able to give us really real time analytics and influencing. The other cool thing about this too is you know we're going from sort of more looking backwards and making business analytics based on what has already happened in the past to being able to do that in the very near past. And of course now with modern analytics and models that are being created for ai we're able to do more predictive analytics so we can actually identify errors, identify challenges before they even occur based on pattern matching that they're saying. Um So it's really opening up new doors and new areas that we've never been able to see before that's really all powered by by these capabilities. >>It's insane the amount of data that is coming. We think data is overwhelming today. You ain't seen nothing yet. Um Now erin you cover the edge and the telecom business up. I was beside it when I when I when I found that out because the telecom businesses is ripe for transformation. Um So what do you how is Dell thinking about that? Why are you sort of putting those together? What are the synergies that you see in in the commonalities in those 22 sectors? >>Yeah. I mean at the end of the day it's really all about serving the enterprise customers in the in the organizations of all kinds um that the industry is trying to bring these edge technologies too and that's no different with the telecommunications industry. Right? So you know when when the when the four G world changed about 10 years ago um you know the telecom industry was able to bring the plumbing the network piping out to all the endpoints but they really didn't capture the over the top revenue opportunities that Four G technologies opened up right. That really went to the hyper scholars. It went to you know, a lot of the companies that we all know and love like uh you know, Uber and Airbnb and netflix and others um and that really when the four Gr that was really more about opening up consumer opportunities as we move to five G. And as we move these ultra low latency and high bandwidth capabilities out to the enterprise edge, it's really the B two B opportunities that are opening up and so on the telecom side we're partnering with the telecommunication companies to modernize their network, enroll five G. L. Quickly. But one of the more important things is that we're partnering with them to be able to build services over the top of that that they can then sell into their customer base and their business customer base. So whether that's mech, whether that's private mobility, um delivering data services over the top of those networks, there's a tremendous opportunity for the telecoms to be able to go and capture um Ed revenue opportunities and we're here to help them to partner with them to be able to do that. Now if you put yourself in the shoes of the customer, the enterprise business, a manufacturer or retail, who's looking to be able to leverage these technologies, there's a variety of ways in which they're going to be able to to to consume these technologies. In some cases they'll be getting it direct from vendors direct from Dell Technologies and others. They might be using solutions integrators to be able to combine these technologies together for a particular solution. They may get some of those technologies from their telecom provider and even others, they might get it from the cloud provider. So um Dell wants to make sure that we're being able to help our customers across a variety of ways in which they want to consume those technologies and we have to businesses focused on that. We've got one business focused on edge solutions where we partner with oT vendors closely as well as cloud providers to be able to provide a technology and infrastructure based on which we can consolidate edge workloads To be able to allow customers that want to be able to run those um those services on prem and by those from a direct vendor. Um there's other customers that want to get those through the telecoms. And so we work closely with the telecommunication providers to provide them that modern cloud native disaggregated network that they're looking to build to support 5G. And then help them build those services on the top that they can sell either way whether the customer wants to get that from a vendor like Dell or from a service provider like like uh like an A T and T and Verizon or others. Um Dell looks to partner with them and be a way to provide that underlying infrastructure that connects all of that together for them. >>Well, I mean the beauty of the telco networks is their hardened. But the problem for the telco networks is they're they're hardened and so you've got the over over the top vendors bow guarding their network. The cost per bit is coming down, data is going through the roof and the telcos can't, they can't participate in that over the top and get to those subscribers. But with Five G. And the technologies that you're talking about bringing to the telecoms world, they're they're gonna transform and many are going to start competing directly and this is just a whole new world out there. I wonder Aaron if you could talk about um what you're specifically talking about at Del Tech World this year as it relates to Edge. >>Sure. So the both of the businesses hedge in telecom have a couple announcements this year. This this year, Deltek World, um starting with Edge um as you may recall back in uh in in the fall of last year when we had our last technologies world, we announced our intent to launch an edge business. Um so that that was formulated and stood up over the last couple of months and and we're really focusing on a couple of different areas. How do we look at our overall Dell technologies portfolio and be able to bring particular products and solutions that exist already and be able to apply those uh to edge use cases. We're looking at building a platform which would allow us to be able to consolidate a variety of workloads. And of course we're working on partnerships specifically in the ot space to be able to vertical eyes these offers to help particular uh particular industries. Right now we're focusing on manufacturing and retail but we'll expand that over time. So at Del Tech World this year we're launching our first set of of solutions family which is going to be the Dell Technologies manufacturing edge solutions, the first one that's gonna be launching as a reference architecture with PTC um thing works on top of what we're also proud to be announcing this week, which is our apex private cloud offering. So this is the first example of of of a partnership with an O. T. Provider on top of apex private cloud so that we can bring in as a service platform offering to the Enterprise edge uh for manufacturers. And combined with one of the industry's leading oT software vendors of thing works. So that's one of the solutions were doing um we're also looking to launch a product which is we're taking our existing um streaming data platform from our unified storage team and taking that, which was once running in the data center out to edge these cases as well. And that allows us to be able to capture click stream data in manufacturing and other environments, buffer and cash that in a in an appliance and then be able to move that off to a data like for longer term analytics. While it's in that buffered state though we open provide a P. I. S. So that you can actually do real time influencing against those click stream data as it's flowing through the appliance on its way to the data lake for longer term analytics. So those are two key areas that we're gonna be focusing on from an edge perspective on the telecom side. Um we're really this is going to be a big year from us as we move towards creating a common end end five G platform from quarter Iran and then also start focusing on partnerships and ecosystems on top of that platform. Uh last week at Red hat summit we actually announced a reference architecture for red hat. Open shift on top of Dell technologies infrastructure servers and networking. And here at Dell technologies world. This week we're announcing a reference architecture with VM ware. So running VM ware telecom cloud platform. Also on top of Dell technologies. Power edge servers and power such as um so this allows us to create that foundation that open cloud native. These are container and virtual layers on top of our hard work to give that that cloud native disaggregated uh, network claim to be able to now run and build core edge and ran solutions on top of and you'll be hearing more about what we're doing in this space in the coming months. >>Nice. That's great. The open ran stuff is really exciting now, last question. So mobile world Congress, the biggest telco show is coming up in late june Yeah, still on. According to the G S M, a lot of people have tapped out um, and but the cube is planning to be there with a hybrid presence, both virtual and physical. We'll see um I wonder if there's anything you want to talk about just in terms of what's happening in telco telco transformation, you guys got any get any events coming up, what can you tell us? >>Yeah, so we took a close look at mobile world congress and and uh this has been a challenging year for everybody. Um you know, Dell as well as many other vendors made the decision this year that we would actually not participate, but we look forward to participating uh with full gusto next year when it's back in a physical environment. Um So what we've decided to do is we are going to be having our own virtual launch event on june 9th. Um And in that event, the theme of that is going to be the modern ecosystem in the neighboring leveraging the power of open. Um So we'll be talking a little bit more about what we're doing from that open cloud, native network infrastructure and then also talk a little bit more about what Dell technologies looking to do to bring a broad ecosystem of technology vendors together and deliver that ecosystem platform for the telecom industry. So registration actually opens this week at Dell Technologies World. So if you go to Dell technologies dot com can register for the event. Um we're really excited to be talking to the telecom providers and also other hardware and software vendors that are in that space to see how we can work together to really drive this next generation of five G. >>That's awesome. I'll be looking for that and and look forward to collaborating with you on that, bringing your thought leadership and the cube community we would really love to to partner on that. Aaron, thanks so much for coming to the cube. Really exciting area and best of luck to you. >>Right. Thank you. I appreciate the time. >>All right. And thank you for watching everybody says Dave Volonte for the Cubes, continuous coverage of Del Tech World 2021. The virtual version will be right back right after this short break.

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in our Palo Alto studios today for theCUBE conversation. We're talking about data. We're always talking about data and it's really interesting. You know we like to go out and get you the first person insight from the people that start the companies, run the companies, the practitioners and, and, and get the insight directly from them. We also like to go out and get original research and hear from original research. And this is a great opportunity to hear from both. So we're excited to have, and welcome back into the studio. He's Aaron Kalb. He's the co founder of Alation, many time CUBE alumni. Aaron. Great to see you. >> Yeah, thanks for having me. It's good to be here. >> Yeah, it's very cool. But today it's a special, a special thing. We've never done this before with you. You guys are releasing a brand new report called, the Alation State of Data Culture Report. So really interesting report. A lot of great information that we're going to dig in here for the next few minutes. But before we do, tell us kind of the history of this report. This is a, the kind of the inaugural release. What was kind of behind it, why did you guys do this? And give us a little background before we get into the details. >> Absolutely. So, yes, that's exactly right. It's debuting today that we plan to kind of update this research quarterly we going to see the trends over time. And this emerged because, you know, I, part of my job, I talk to chief data officers and chief analytics officers across our customer base and prospects. And I keep hearing anecdotally over and over that establishing a data culture, is often the number one priority for these data leaders and for these organizations. And so we wanted to really say, can we quantify that? Can we agree upon a definition of data culture? And can we create sort of a simple yardstick to more objectively measure where organizations are on this sort of data maturity curve to get it into culture. >> Right. I love it. So you created this data, data index right? The data culture index. And, and I think it's important to look at methodology. I think people, a lot of times go right to the results on reports before talking about the methodologies. And let's talk about the methodologies cause we're supposed to be talking about data, right? So you talked to 300, some odd executives, correct. And I think it's really interesting and you broke it down into three kind of buckets of data literacy, if you will. Data search and discovery, number one, data, two kind of literacy in terms of their ability to work with the data. And then the third bucket is really data governance. And then in, in the form ABCD, you gave him a four point score and basically, are they doing it well? Are they doing it in the majority of the time? Are they doing it about half, they got one or they got a zero and you get this four point scale and you end up with a 12 point scale which we're all familiar with from, from school, from an A to an, A minus and B, et cetera. Just dig it a little bit on those three categories and how you chose those. So the first one again is kind of the data search and discovery, you know can they find it and then their competency, if you will and then a governance and compliance. Kind of dig into each of those three buckets a little bit. >> For sure. So, so the, the end goal in data culture, is to have an organization in which data is valued and decisions are made based on data and evidence, right? Versus a culture in which we go with the highest paid person's opinion or what we did last quarter or any of these other ways things get done. And so the idea is to make that possible, as you said you've to be able to find the data when you need it. That's the data search and discovery. You've to be able to interpret that data correctly and draw valid conclusions from it. And that's a data literacy, excuse me. And both of those are contingent upon having data governance in place. So that data is well-defined and has high data quality, as well as other aspects, so that it is possible to find it and understand it properly. >> Right. And what are the things too that I think is really important that we call that, and again, we're going to dive into the details, is your perceived execution versus the reported execution by the people that are actually providing data. And I think you've found and you've highlighted on specific slides that you know, there's not necessarily a match there. And sometimes that you know, what you perceive is happening, isn't necessarily what's happening when you go down and query the people in the field. So really important to come up with a number. And I think a, I think you said this is going to be an ongoing thing over a period of time. So you kind of start to see longitudinal changes in these organizations. >> Absolutely. And we're very excited to see those, those trends over time. But even at the outset is this you know, very striking effect emerges which is, as you said, if we ask one of these you know, 300 data leaders, you know, all around the world actually, you know, if we ask, how is the data culture at your company overall, and this is very broad general top down way and have them graded on the sort of SaaS scale. You know, we get results where there's a large gap between kind of that level of maturity and what emerges in a bottom up methodology excuse me, in which you ask about, you know governance and literacy and, and such kind of by department and in a more bottom up way. And so we do see that that, you know, it can be helpful, even for data people to have a, a more granular metric and framework for quantifying their progress. >> Right? Let's jump into some of the results. It's, it's a fascinating, they're kind of all over the map, but there's some definite trends. One of the trends you talked about is that there's a lot of questions on the quality of the data. But that's a real inhibitor to people. Whether that suspicion is because it's not good data. And I don't know, this question for you, is, is, do they think it's not relevant to the decision that's being made? Is it an incomplete data set or the wrong data set? It seems to be that keeps coming up over and over about, decision-makers not necessarily having confidence in the data. What, can you share a little bit more color around that? >> Yeah, it's quite interesting actually. So what we find is that 90%. So 90 people, 10 executives (indistinct) to question the data sometimes often or always. But the part that's maybe disappointing or concerning is the two thirds of executives are believed to ignore the data and make a decision kind of pushing the data aside which is really quite striking when you think about it, why have all this data, if more often than not you're sort of disregarding it to make your final answer. And so you're absolutely correct when we dug into why, what are the reasons behind pushing it aside. Data quality was number one. And I think it is a question of, Oh, is the data inaccurate? Is it out of date, these sort of concerns sort of we, we hear from customers and prospects. But as we dig in deeper in the survey results, excuse me, we, we see some other reasons behind that. One is a lack of collaboration between the data analytics folks and the business folks. And so there's a question of, I don't know exactly where this data came from or to your point kind of how it was produced. What was the methodology? How was it sourced? And maybe because of that disconnect is a lack of trust. So trust really is the ultimate I think, failure to having data culture really take root. >> Right? And it's trust in this trust, as you said, not only in the data per se, the source of the data, the quality of the data, the relevance of the data but also the people who are providing you with the data. And obviously you get, you get some data sets. Sometimes you didn't get other data sets. So, that's really I'm a little bit disconcerting. The other thing I thought was kind of interesting is, it seems to be consistent that the, the primary reason that people are using big data projects is around operations and operations efficiency, a little bit about compliance, but, you know, it's interesting we had you on at the MIT CDOIQ, Chief Data Information Officer quality symposium, and you talked about the goodness of people moving from kind of a defensive posture to an offensive posture, you know using data in terms of product development and innovation. And, and what comes across in this survey is that's kind of down the list behind you know, kind of operational efficiency. We're seeing a little bit of governance and regulation but the, the quest for data as a tool for innovation, didn't really shine through in this report. >> Well, you know, it's very interesting. It depends whether you look at the aggregate level or you break things down a little bit more. So one thing we did after we got that zero to 12 scale on the data culture index or DCI, is it actually, we were able to break it down into thirds. And among the sort of bottom third, it has the least well-established data culture by this yardstick. We've found that governance and regulatory compliance, was the number one application of data. But among the top third of respondents, we actually found the opposite where things like providing a great customer experience, doing product innovation, those sort of things actually came to the fore and governance fell behind. So I think there is this curve where, It's table stakes to get the sort of defense side of data figured out. And then you can move on to offense in using data to make your organization meet its meet its other goals. >> Right. Right. And then I wanted to get your take on kind of the democratization of data, right? This is a, this is a trend that's been going on, and really, I think you said before you know, your guys' whole mission is to empower curious and rational world to give people the ability to ask the right questions have the right data and get the right answer. So, you know, we've seen democratization in terms of the access to the data, the access to the tools, the ability to do something with the data and the tool, and then the actual authority to execute business decision based on that. The results on that seem a little bit split here because a lot of the problems seem to be focused on leadership, not necessarily taking a data based decision move, but on the good hand a lot of people trying to break down data silos and make data more accessible for a larger group of people. So that more people in the organization are making data based decisions. This seems kind of like this little bit of a bifurcation between the C suite and everybody else trying to get their job done. >> Absolutely. There's always this question of you know, sort of the, that organizational wide initiative and then what's happening on the ground. One thing we saw that was very heartening and aligns with our customers index success, is a real emphasis being placed on having data governance and data context and data literacy factors sort of be embedded at the point of use. To not expecting people, to just like take a course and look things up and kind of end up with their workflow to be able to use data quickly and accurately and, and interpret it in varied ways. So that was really exciting to see as, as, as a initiative. It sort of bridges that gap along with initiatives to have more collaboration and integration between the data people and the business people. because really you know, they exist to serve one another. But in terms of the disconnect between the C suite and other parts of the org, there was a really interesting inverse correlation. Well, or maybe it's not interesting how you look at it, but basically, you know, when we talk to C level executives and ask, you know, does the C suite ignore data? Do they question data et cetera, those numbers came in lower than when we talked to, you know, senior director about the C suite right? It's sort of the farther you get, and there's a difference there, you know, from my perspective, I almost wonder whether that distance is actually is more objective viewpoint. And when you're in that role, it's hard to even see your cognitive biases and your tendency to ignore a data when it doesn't suit you. >> Right. Right. So there's, there's some other interesting things here. So one of them is, you know, kind of predictors, right? One of the whole reasons to do studies and collect data so that we can have some predictive ability. And, and it comes out here that the reporting structure is a strong predictor of a company's data tier structure. So, you know, there's the whole rise of the chief data officers and the chief analytics officer and the chief data and analytics officer and lots of conversations about those roles and what exactly are those roles and who do they report to. Your study finds a pretty compelling leading indicator that if that role is reporting to either the CEO or the executive board, which is often a one in the same person, that that's actually a terrific indicator of success in moving to a more data centric culture. >> That's absolutely correct. So we found that that top third of organizations on the data culture index were much more likely to have a chief data executive, a CDO, CAO or CDAO. In fact, they're more likely to have folks with the analytics in their title because in some organizations, data is thought to mean sort of raw data, infrastructural defense and analytics is sort of where it gets you know, infused into business processes and value. But certainly that top third is much more likely to have the chief data executive reporting into the executive board or CEO when the highest ranking data executive is under the CIO or some other part of the organization, those orgs tend to score a far lower on the DCI. >> Right. Right. So it's interesting, you know you're a really interesting guy even doing this for a while. You were at Siri before you were at Alation. So you have a really good feel for kind of what data can do and can't do and natural human or natural language processing and, and, and human voice interaction with these devices, a really interesting case study, and they can do a really good job within a small defined data set and instruction set, but they don't do necessarily so well once you kind of get outside how, how they're trained. And you've talked a lot about how metaphor shaped the way that we think and I know you and Dave talked about data oil and data lakes I don't want to necessarily go down that whole path but I do think it's important. And what came out of the study and the way people think about data. You know, there's a lot of conversation. How do you value data? Is data, you know it used to just be an expense that we had to buy servers to store the stuff we weren't sure what we ever did with it. So I wonder if there's any, you know, kind of top level metaphors level, kind of a thought or process or framing in the companies that you study that came out. maybe not necessarily in the top line data, but maybe in some of the notes that help define why some people, you know are being successful at making this transition and putting, you know kind of data out front of their decision processing versus data, either behind as a supporting thing or maybe data, I just don't have time with it or I don't trust it, or God knows where you got that, and this is not the data that I wanted. You know, was there any, you know, kind of tangental or anecdotal stuff that came out of this study that's more reflective of, of the softer parts of a data culture versus the harder parts in terms of titles and roles and, and, and job responsibilities. >> Yeah. It's a really interesting place to explore. I do think there's a, I don't want to make this overly simplistic group binary, but at the end of the day you know, like anything else within an organization, you can view data as a liability to say, okay, we have for example, you know, customer's names and phone numbers and passwords, and we just need to prevent an adverse event in which there's a leak or some sort of InfoSec problem that could cause, you know, bad press and fines and other negative consequences. And I think the issue there is if data's a liability, the most you know, the best case is that it's worth zero as opposed to some huge negative on your company's balance sheet. And, and I think, you know, intuitively, if you really want to prevent data misuse and data problems, one fail safe, but I think ultimately in its own way risky way to do that was just not collect any data, right. And not store it. So I think that the transition is to say, look data must be protected and taken care of that's step zero. But you know, it's really just the beginning and data is this asset that can be used to inform the huge company level strategic decisions that are made in annual planning at the board level, down to the millions of little decisions every day in the work of people in customer support and in sales and in product management and in, you know, various roles that just across industries. And I think once you have that, that shift, you know the upside is potentially, you know, unbounded. >> Right. And, and it just changes the way, the way you think. And suddenly instead of saying, Oh, data needs to be kind of hidden away, it's more like, Oh, people need to be trained on data use and empowered with data. And it's all about not if it's used or if it's misused but really how it's used and why it's used, what it's being used for to make a real impact. >> Right. Right. And it's funny when I just remember it being back in business school one of the great things that help teach is to think in terms of data, right. And you always have the infamous center consulting interview question, How many manhole covers are in Manhattan. Right. So, you know, to, to, to start to think about that problem from a data centric, point of view really gives you a leg up and, and even, you know where to start and how to attack those types of problems. And I thought it was interesting you know, talking about challenges for people to have a more data centric, point of view. It's interesting. The reports says, basically everybody said there's all kinds of challenges around data quality and compliance, and they had democratization. But the bottom companies, the bottom companies said that the biggest challenge was lack of buy in from company leadership. So I guess the good news bad news is that there's a real opportunity to make a significant change and get your company from the bottom third to a middle third or a top third, simply by taking a change in attitude about putting data in a much more central role in your decision making process. 'Cause all the other stuff's kind of operational, execution challenges that we all have, not enough people, blah, blah, blah. But in terms of attitude of leadership and prioritization, that's something that's very easy to change if you so choose. And really seems to be the key to unlock this real journey as opposed to the minutiae of a lot of the little details that that are a challenge for everybody. >> Absolutely. In your changing attitudes might be the easiest thing or the hardest thing depending on (indistinct). But I think you're absolutely right. The first step, which, which which could, maybe it should be easy, is admitting that you have a problem or maybe to put it more positively, realizing you have an opportunity. >> I love that. And then just again, looking at the top tier companies, the other thing that I thought was pretty interesting in this study is, I'm looking at it here, is getting champions in each of the operational segments. So rather than, I mean, a chief data officer is important and you know, somebody kind of at the high level to shepherd it in the executive suite, as we just discussed, but within each of the individual tasks and functions and roles, whether that's operations or customer service or product development or operational efficiency, you need some type of champion, some type of person, you know, banging the gavel, collecting the data, smoothing out the complexities, helping people get their thing together. And again, another way to really elevate your position on the score. >> Absolutely. And I think this idea of again, bridging between, you know, if data is centralized you have a chance to try to really get excellent practices within the data org. But even it becomes even more essential to have those ambassadors, people who are in the business and understand all the business context who can sort of make the data relevant, identify the key areas where data can really help, maybe demystify data and pick the right metaphors and the right examples to make it real for the people in their function. >> Right. Right. So Aaron has a lot of great stuff. People can go to the website at alation.com. I'm sure you'll have a link to this, a very prominently displayed, but, and they should and they should check it out and really think about it and think about how it applies to their own situation, their own department, company et cetera. I just wanted to give you the last word before we before we sign off, you know, kind of what was the most you know, kind of positive affirmation or not the most but one or two of the most outcome affirming outcomes of this exercise. And what were one or two of the things that were a little concerning or, you know, kind of surprises on the downside that, that came out of this research? >> Yeah. So I think one thing that was maybe surprising or concerning the biggest one is sort of where we started with that disconnect between, you know, what people would, say as an off the cuff overall assessment and the disconnect between that and what emerges when we go department by department and (indistinct) to be pillars of data culture from such a discovery to data literacy, to data governance. I think that disconnect, you know, should give one pause. I think certainly it should make one think, Hmm. Maybe I shouldn't look from 10,000 feet, but actually be a little more systematic. And considering the framework I use to assess data culture that is the most important thing to my organization. I think though, there's this quote that you move what you measure, just having this hopefully simple but not simplistic yardstick to measure data culture and the data culture index should help people be a little bit more realistic in their quantification and they track their progress, you know, quarter over quarter. So I think that's very promising. I think another thing is that, you know sometimes we ask, how long have you had this initiative? How much progress have you made? And it can sometimes seem like pushing a boulder uphill. Obviously the COVID pandemic and the economic impacts of that has been really tragic and really hard. You know, a tiny silver lining in that is the survey results showed that organizations have really observed a shift in how much they're using data because sometimes things are changing but it's like a frog in boiling water. You don't realize it. And so you just assume that the future is going to look like the recent past and you don't look at the data or you ignore the data or you miss parts of the data. And a lot of organizations said, you know COVID was this really troubling wake up call, but they could even after this crisis is over, producing enduring change which people were consulting data more and making decisions in a more data driven way. >> Yeah, certainly an accelerant that, that is for sure whether you wanted it, didn't want it, thought you had it at the time, didn't have time. You know COVID is definitely digital transformation accelerant and data is certainly the thing that powers that. Well again, it's the Alation State of Data Culture Report available, go check it at alation.com. Aaron always great to catch up and again, thank you for, for doing the work and supporting this research. And I think it's really important stuff. And it's going to be interesting to see how it changes over time. 'Cause that's really when these types of reports really start to add value. >> Thanks for having me, Jeff and I really look forward to discussing some of those trends as the research is completed. >> All right. Thanks a lot, Aaron, take care. Alright. He's Aaron and I'm Jeff. You're watching theCUBE, Palo Alto. Thanks for watching. We'll see you next time. (upbeat music)

>> Announcer: From theCUBE studios in Palo Alto, in Boston, connecting with thought leaders all around the world. This is theCUBE conversation. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're in our Palo Alto studios today for theCUBE conversation. We're talking about data. We're always talking about data and it's really interesting. You know we like to go out and get you the first person insight from the people that start the companies, run the companies, the practitioners and, and, and get the insight directly from them. We also like to go out and get original research and hear from original research. And this is a great opportunity to hear from both. So we're excited to have, and welcome back into the studio. He's Aaron Kalb. He's the co founder of Alation, many time CUBE alumni. Aaron. Great to see you. >> Yeah, thanks for having me. It's good to be here. >> Yeah, it's very cool. But today it's a special, a special thing. We've never done this before with you. You guys are releasing a brand new report called, the Alation State of Data Culture Report. So really interesting report. A lot of great information that we're going to dig in here for the next few minutes. But before we do, tell us kind of the history of this report. This is a, the kind of the inaugural release. What was kind of behind it, why did you guys do this? And give us a little background before we get into the details. >> Absolutely. So, yes, that's exactly right. It's debuting today that we plan to kind of update this research quarterly we going to see the trends over time. And this emerged because, you know, I, part of my job, I talk to chief data officers and chief analytics officers across our customer base and prospects. And I keep hearing anecdotally over and over that establishing a data culture, is often the number one priority for these data leaders and for these organizations. And so we wanted to really say, can we quantify that? Can we agree upon a definition of data culture? And can we create sort of a simple yardstick to more objectively measure where organizations are on this sort of data maturity curve to get it into culture. >> Right. I love it. So you created this data, data index right? The data culture index. And, and I think it's important to look at methodology. I think people, a lot of times go right to the results on reports before talking about the methodologies. And let's talk about the methodologies cause we're supposed to be talking about data, right? So you talked to 300, some odd executives, correct. And I think it's really interesting and you broke it down into three kind of buckets of data literacy, if you will. Data search and discovery, number one, data, two kind of literacy in terms of their ability to work with the data. And then the third bucket is really data governance. And then in, in the form ABCD, you gave him a four point score and basically, are they doing it well? Are they doing it in the majority of the time? Are they doing it about half, they got one or they got a zero and you get this four point scale and you end up with a 12 point scale which we're all familiar with from, from school, from an A to an, A minus and B, et cetera. Just dig it a little bit on those three categories and how you chose those. So the first one again is kind of the data search and discovery, you know can they find it and then their competency, if you will and then a governance and compliance. Kind of dig into each of those three buckets a little bit. >> For sure. So, so the, the end goal in data culture, is to have an organization in which data is valued and decisions are made based on data and evidence, right? Versus a culture in which we go with the highest paid person's opinion or what we did last quarter or any of these other ways things get done. And so the idea is to make that possible, as you said you've to be able to find the data when you need it. That's the data search and discovery. You've to be able to interpret that data correctly and draw valid conclusions from it. And that's a data literacy, excuse me. And both of those are contingent upon having data governance in place. So that data is well-defined and has high data quality, as well as other aspects, so that it is possible to find it and understand it properly. >> Right. And what are the things too that I think is really important that we call that, and again, we're going to dive into the details, is your perceived execution versus the reported execution by the people that are actually providing data. And I think you've found and you've highlighted on specific slides that you know, there's not necessarily a match there. And sometimes that you know, what you perceive is happening, isn't necessarily what's happening when you go down and query the people in the field. So really important to come up with a number. And I think a, I think you said this is going to be an ongoing thing over a period of time. So you kind of start to see longitudinal changes in these organizations. >> Absolutely. And we're very excited to see those, those trends over time. But even at the outset is this you know, very striking effect emerges which is, as you said, if we ask one of these you know, 300 data leaders, you know, all around the world actually, you know, if we ask, how is the data culture at your company overall, and this is very broad general top down way and have them graded on the sort of SaaS scale. You know, we get results where there's a large gap between kind of that level of maturity and what emerges in a bottom up methodology excuse me, in which you ask about, you know governance and literacy and, and such kind of by department and in a more bottom up way. And so we do see that that, you know, it can be helpful, even for data people to have a, a more granular metric and framework for quantifying their progress. >> Right? Let's jump into some of the results. It's, it's a fascinating, they're kind of all over the map, but there's some definite trends. One of the trends you talked about is that there's a lot of questions on the quality of the data. But that's a real inhibitor to people. Whether that suspicion is because it's not good data. And I don't know, this question for you, is, is, do they think it's not relevant to the decision that's being made? Is it an incomplete data set or the wrong data set? It seems to be that keeps coming up over and over about, decision-makers not necessarily having confidence in the data. What, can you share a little bit more color around that? >> Yeah, it's quite interesting actually. So what we find is that 90%. So 90 people, 10 executives (indistinct) to question the data sometimes often or always. But the part that's maybe disappointing or concerning is the two thirds of executives are believed to ignore the data and make a decision kind of pushing the data aside which is really quite striking when you think about it, why have all this data, if more often than not you're sort of disregarding it to make your final answer. And so you're absolutely correct when we dug into why, what are the reasons behind pushing it aside. Data quality was number one. And I think it is a question of, Oh, is the data inaccurate? Is it out of date, these sort of concerns sort of we, we hear from customers and prospects. But as we dig in deeper in the survey results, excuse me, we, we see some other reasons behind that. One is a lack of collaboration between the data analytics folks and the business folks. And so there's a question of, I don't know exactly where this data came from or to your point kind of how it was produced. What was the methodology? How was it sourced? And maybe because of that disconnect is a lack of trust. So trust really is the ultimate I think, failure to having data culture really take root. >> Right? And it's trust in this trust, as you said, not only in the data per se, the source of the data, the quality of the data, the relevance of the data but also the people who are providing you with the data. And obviously you get, you get some data sets. Sometimes you didn't get other data sets. So, that's really I'm a little bit disconcerting. The other thing I thought was kind of interesting is, it seems to be consistent that the, the primary reason that people are using big data projects is around operations and operations efficiency, a little bit about compliance, but, you know, it's interesting we had you on at the MIT CDOIQ, Chief Data Information Officer quality symposium, and you talked about the goodness of people moving from kind of a defensive posture to an offensive posture, you know using data in terms of product development and innovation. And, and what comes across in this survey is that's kind of down the list behind you know, kind of operational efficiency. We're seeing a little bit of governance and regulation but the, the quest for data as a tool for innovation, didn't really shine through in this report. >> Well, you know, it's very interesting. It depends whether you look at the aggregate level or you break things down a little bit more. So one thing we did after we got that zero to 12 scale on the data culture index or DCI, is it actually, we were able to break it down into thirds. And among the sort of bottom third, it has the least well-established data culture by this yardstick. We've found that governance and regulatory compliance, was the number one application of data. But among the top third of respondents, we actually found the opposite where things like providing a great customer experience, doing product innovation, those sort of things actually came to the fore and governance fell behind. So I think there is this curve where, It's table stakes to get the sort of defense side of data figured out. And then you can move on to offense in using data to make your organization meet its meet its other goals. >> Right. Right. And then I wanted to get your take on kind of the democratization of data, right? This is a, this is a trend that's been going on, and really, I think you said before you know, your guys' whole mission is to empower curious and rational world to give people the ability to ask the right questions have the right data and get the right answer. So, you know, we've seen democratization in terms of the access to the data, the access to the tools, the ability to do something with the data and the tool, and then the actual authority to execute business decision based on that. The results on that seem a little bit split here because a lot of the problems seem to be focused on leadership, not necessarily taking a data based decision move, but on the good hand a lot of people trying to break down data silos and make data more accessible for a larger group of people. So that more people in the organization are making data based decisions. This seems kind of like this little bit of a bifurcation between the C suite and everybody else trying to get their job done. >> Absolutely. There's always this question of you know, sort of the, that organizational wide initiative and then what's happening on the ground. One thing we saw that was very heartening and aligns with our customers index success, is a real emphasis being placed on having data governance and data context and data literacy factors sort of be embedded at the point of use. To not expecting people, to just like take a course and look things up and kind of end up with their workflow to be able to use data quickly and accurately and, and interpret it in varied ways. So that was really exciting to see as, as, as a initiative. It sort of bridges that gap along with initiatives to have more collaboration and integration between the data people and the business people. because really you know, they exist to serve one another. But in terms of the disconnect between the C suite and other parts of the org, there was a really interesting inverse correlation. Well, or maybe it's not interesting how you look at it, but basically, you know, when we talk to C level executives and ask, you know, does the C suite ignore data? Do they question data et cetera, those numbers came in lower than when we talked to, you know, senior director about the C suite right? It's sort of the farther you get, and there's a difference there, you know, from my perspective, I almost wonder whether that distance is actually is more objective viewpoint. And when you're in that role, it's hard to even see your cognitive biases and your tendency to ignore a data when it doesn't suit you. >> Right. Right. So there's, there's some other interesting things here. So one of them is, you know, kind of predictors, right? One of the whole reasons to do studies and collect data so that we can have some predictive ability. And, and it comes out here that the reporting structure is a strong predictor of a company's data tier structure. So, you know, there's the whole rise of the chief data officers and the chief analytics officer and the chief data and analytics officer and lots of conversations about those roles and what exactly are those roles and who do they report to. Your study finds a pretty compelling leading indicator that if that role is reporting to either the CEO or the executive board, which is often a one in the same person, that that's actually a terrific indicator of success in moving to a more data centric culture. >> That's absolutely correct. So we found that that top third of organizations on the data culture index were much more likely to have a chief data executive, a CDO, CAO or CDAO. In fact, they're more likely to have folks with the analytics in their title because in some organizations, data is thought to mean sort of raw data, infrastructural defense and analytics is sort of where it gets you know, infused into business processes and value. But certainly that top third is much more likely to have the chief data executive reporting into the executive board or CEO when the highest ranking data executive is under the CIO or some other part of the organization, those orgs tend to score a far lower on the DCI. >> Right. Right. So it's interesting, you know you're a really interesting guy even doing this for a while. You were at Siri before you were at Alation. So you have a really good feel for kind of what data can do and can't do and natural human or natural language processing and, and, and human voice interaction with these devices, a really interesting case study, and they can do a really good job within a small defined data set and instruction set, but they don't do necessarily so well once you kind of get outside how, how they're trained. And you've talked a lot about how metaphor shaped the way that we think and I know you and Dave talked about data oil and data lakes I don't want to necessarily go down that whole path but I do think it's important. And what came out of the study and the way people think about data. You know, there's a lot of conversation. How do you value data? Is data, you know it used to just be an expense that we had to buy servers to store the stuff we weren't sure what we ever did with it. So I wonder if there's any, you know, kind of top level metaphors level, kind of a thought or process or framing in the companies that you study that came out. maybe not necessarily in the top line data, but maybe in some of the notes that help define why some people, you know are being successful at making this transition and putting, you know kind of data out front of their decision processing versus data, either behind as a supporting thing or maybe data, I just don't have time with it or I don't trust it, or God knows where you got that, and this is not the data that I wanted. You know, was there any, you know, kind of tangental or anecdotal stuff that came out of this study that's more reflective of, of the softer parts of a data culture versus the harder parts in terms of titles and roles and, and, and job responsibilities. >> Yeah. It's a really interesting place to explore. I do think there's a, I don't want to make this overly simplistic group binary, but at the end of the day you know, like anything else within an organization, you can view data as a liability to say, okay, we have for example, you know, customer's names and phone numbers and passwords, and we just need to prevent an adverse event in which there's a leak or some sort of InfoSec problem that could cause, you know, bad press and fines and other negative consequences. And I think the issue there is if data's a liability, the most you know, the best case is that it's worth zero as opposed to some huge negative on your company's balance sheet. And, and I think, you know, intuitively, if you really want to prevent data misuse and data problems, one fail safe, but I think ultimately in its own way risky way to do that was just not collect any data, right. And not store it. So I think that the transition is to say, look data must be protected and taken care of that's step zero. But you know, it's really just the beginning and data is this asset that can be used to inform the huge company level strategic decisions that are made in annual planning at the board level, down to the millions of little decisions every day in the work of people in customer support and in sales and in product management and in, you know, various roles that just across industries. And I think once you have that, that shift, you know the upside is potentially, you know, unbounded. >> Right. And, and it just changes the way, the way you think. And suddenly instead of saying, Oh, data needs to be kind of hidden away, it's more like, Oh, people need to be trained on data use and empowered with data. And it's all about not if it's used or if it's misused but really how it's used and why it's used, what it's being used for to make a real impact. >> Right. Right. And it's funny when I just remember it being back in business school one of the great things that help teach is to think in terms of data, right. And you always have the infamous center consulting interview question, How many manhole covers are in Manhattan. Right. So, you know, to, to, to start to think about that problem from a data centric, point of view really gives you a leg up and, and even, you know where to start and how to attack those types of problems. And I thought it was interesting you know, talking about challenges for people to have a more data centric, point of view. It's interesting. The reports says, basically everybody said there's all kinds of challenges around data quality and compliance, and they had democratization. But the bottom companies, the bottom companies said that the biggest challenge was lack of buy in from company leadership. So I guess the good news bad news is that there's a real opportunity to make a significant change and get your company from the bottom third to a middle third or a top third, simply by taking a change in attitude about putting data in a much more central role in your decision making process. 'Cause all the other stuff's kind of operational, execution challenges that we all have, not enough people, blah, blah, blah. But in terms of attitude of leadership and prioritization, that's something that's very easy to change if you so choose. And really seems to be the key to unlock this real journey as opposed to the minutiae of a lot of the little details that that are a challenge for everybody. >> Absolutely. In your changing attitudes might be the easiest thing or the hardest thing depending on (indistinct). But I think you're absolutely right. The first step, which, which which could, maybe it should be easy, is admitting that you have a problem or maybe to put it more positively, realizing you have an opportunity. >> I love that. And then just again, looking at the top tier companies, the other thing that I thought was pretty interesting in this study is, I'm looking at it here, is getting champions in each of the operational segments. So rather than, I mean, a chief data officer is important and you know, somebody kind of at the high level to shepherd it in the executive suite, as we just discussed, but within each of the individual tasks and functions and roles, whether that's operations or customer service or product development or operational efficiency, you need some type of champion, some type of person, you know, banging the gavel, collecting the data, smoothing out the complexities, helping people get their thing together. And again, another way to really elevate your position on the score. >> Absolutely. And I think this idea of again, bridging between, you know, if data is centralized you have a chance to try to really get excellent practices within the data org. But even it becomes even more essential to have those ambassadors, people who are in the business and understand all the business context who can sort of make the data relevant, identify the key areas where data can really help, maybe demystify data and pick the right metaphors and the right examples to make it real for the people in their function. >> Right. Right. So Aaron has a lot of great stuff. People can go to the website at alation.com. I'm sure you'll have a link to this, a very prominently displayed, but, and they should and they should check it out and really think about it and think about how it applies to their own situation, their own department, company et cetera. I just wanted to give you the last word before we before we sign off, you know, kind of what was the most you know, kind of positive affirmation or not the most but one or two of the most outcome affirming outcomes of this exercise. And what were one or two of the things that were a little concerning or, you know, kind of surprises on the downside that, that came out of this research? >> Yeah. So I think one thing that was maybe surprising or concerning the biggest one is sort of where we started with that disconnect between, you know, what people would, say as an off the cuff overall assessment and the disconnect between that and what emerges when we go department by department and (indistinct) to be pillars of data culture from such a discovery to data literacy, to data governance. I think that disconnect, you know, should give one pause. I think certainly it should make one think, Hmm. Maybe I shouldn't look from 10,000 feet, but actually be a little more systematic. And considering the framework I use to assess data culture that is the most important thing to my organization. I think though, there's this quote that you move what you measure, just having this hopefully simple but not simplistic yardstick to measure data culture and the data culture index should help people be a little bit more realistic in their quantification and they track their progress, you know, quarter over quarter. So I think that's very promising. I think another thing is that, you know sometimes we ask, how long have you had this initiative? How much progress have you made? And it can sometimes seem like pushing a boulder uphill. Obviously the COVID pandemic and the economic impacts of that has been really tragic and really hard. You know, a tiny silver lining in that is the survey results showed that organizations have really observed a shift in how much they're using data because sometimes things are changing but it's like a frog in boiling water. You don't realize it. And so you just assume that the future is going to look like the recent past and you don't look at the data or you ignore the data or you miss parts of the data. And a lot of organizations said, you know COVID was this really troubling wake up call, but they could even after this crisis is over, producing enduring change which people were consulting data more and making decisions in a more data driven way. >> Yeah, certainly an accelerant that, that is for sure whether you wanted it, didn't want it, thought you had it at the time, didn't have time. You know COVID is definitely digital transformation accelerant and data is certainly the thing that powers that. Well again, it's the Alation State of Data Culture Report available, go check it at alation.com. Aaron always great to catch up and again, thank you for, for doing the work and supporting this research. And I think it's really important stuff. And it's going to be interesting to see how it changes over time. 'Cause that's really when these types of reports really start to add value. >> Thanks for having me, Jeff and I really look forward to discussing some of those trends as the research is completed. >> All right. Thanks a lot, Aaron, take care. Alright. He's Aaron and I'm Jeff. You're watching theCUBE, Palo Alto. Thanks for watching. We'll see you next time. (upbeat music)

>> Narrator: From the Cube Studios in Palo Alto and Boston, it's the Cube, covering empowering the autonomous enterprise, brought to you by Oracle Consulting. >> Hi everybody, welcome back to this special digital presentation where we are tracking the transformation of Oracle Consulting. Aaron Millstone is back, he's the Senior Vice President of Oracle Consulting and he's joined by Jeff Davis whose Principal at Deloitte, he's the Chief Commercial Officer for Oracle at Deloitte. Gentlemen, good to see you, welcome. We see a lot of these deals, sometimes we call them Barney deals, you know, I love you, you love me, there's a press release and that's it. But, so one of the things we look for, okay, is there teeth behind this? Now, you guys have come up with what you call Elevate. What is Elevate, how did it get started? Then I have some follow up questions. >> Well, Elevate really got started when Aaron and I started to look at the assets that each of the firms possessed. On the Deloitte side as Aaron suggested, we have deep capabilities and a broad range of technologies. Some of them could be technologies with Oracle. At the same time, we didn't have a great deal of depth in Oracle's technical products, Oracle Cloud infrastructure and Oracle Autonomous. Our bench was not as big as Aaron's. And Aaron also had access to Oracle development at a level that we didn't have access to. So we really found ourselves in a situation where we could put those two capabilities together and we could offer something to our clients. And the broad rage of Oracle customers in the field, they had access to all of Deloitte's capabilities, which include great project management, great change management, real skill around the strategic aspects of cloud migration and Aaron had tools and had resources trained and developed around the latest Oracle technology. They'd always be a step ahead of any SI. So together we felt this was really a differentiation for the market place. >> One of the things we look for, is there any other integration? Are you doing co-engineering? In this case maybe not co-engineering, but are there tools that you're developing that you're taking to market that you're actually leveraging? Aaron, can you talk about that a little bit and convince us it's not just a sales play? >> Yeah, sure, and Jeff eluded to some of this earlier too, right. So, we definitely each had our respective tool line, right? Deloitte's investments and tools, one of them's called ATADATA, that we've seen used quite a few times now. We've invested in something we called Oracle Soar. You know, our tools are, as you'd imagine, heavily Oracle focused. It's about moving Oracle technology to Oracle cloud. ATADATA and some of the tools that Deloitte's invested in are focused more comprehensively on wholistically at looking at everything in a data center and everything that's across data centers and start to develop a set of facts around this stuff. But in both cases we actually looked at these things and we said, you know what if you combine these together, we get a very comprehensive view of what exactly it is that we're looking at with a customer. So we can tell everything from the types of traffic we see on the network, to the specific versions of stuff. We can start to identify whether there's risk associated with having things not patched or out of support, but again a very comprehensive view that's based on facts. And so, you know, we took those tools and we combined them together so that we can go in to a customer and give a complete end to end view from both an Oracle and Deloitte perspective. And quite frankly it doesn't matter whether Deloitte leads or whether Oracle leads, we've developed these tools together, we're going to market together, and we've even got, you know, the templates you'd expect consultancies to have right? So when you look at business cases, we've got joint business case templates that we've created together and that we're using actively with customers and then we're refining them and improving them each time we do it. But, you know, we're at a point now where our tools are combined, our templates are combined and we even at this, you know, we were even- Jeff and I were on a call earlier, yesterday actually, we even got a joint war room that's constantly engaging with different account teams and making sure that we structurally approach things in a consistent way so that we're driving business value and using the tools appropriately. >> Aaron you and I have talked about, you know, data centers and building data centers and investing; it's just not a good use of capital today. There are so many other things that organizations can do. You guys have identified data center consolidation as, I'll call it a you know, an initiative that you're seeing customers. I wondered if you could talk about that a little bit, is that kind of a starting point for conversations? >> Yeah, well it's definitely a starting point. So we call it and refer to it as infrastructure lead transformation and the appetite for that is certainly high. We're seeing an increased focus on you know, what do customers need to do to take not just a workload here and there, but how to they get out of the data center business hole? So it's sort of, it's a forgone conclusion. Like you just said, it's not really a question of should we invest in another data center, or should we invest in up-tooling our data centers? The question has changed to, let's move to cloud, how do we get there? And let's move in a big way. And that's, we're seeing that dialogue across all of our customers. And quite frankly, even for Oracle, it's been a learning curve for us, right? We started with an Oracle workload conversation, which is: do you want to move this Oracle workload to Oracle's cloud? Do you want to move that Oracle workload to Oracle's cloud? And really what we're finding is it's a wholesale transformation of everything in a data center to one or more clouds right? Again, often it's a multicloud strategy and that's okay. And we, you know, we're having more-bigger conversations. The thing that has been really interesting is these conversations have evolved and especially as we work with our partners at Deloitte, has been that, you know, we think that the combination of our cloud technology, the consulting services that Oracle consulting and Deloitte can bring to bear and then Oracle's ability to finance the whole deal, makes some very compelling conversations for customers cause you can walk in to a CIO, to a CFO and say look on day one, you can actually have a lower spend than what you have today in your data center, and get a cloud transformation on Deloitte at the same time. >> Let's talk a little bit more about that business case. Is that generally what you're seeing where it starts as let's take some costs right out? And then, Aaron, you and I talked about maybe investing that in the future, but is that really the starting point for the vast majority of customers? Let's cut some costs right away and get a payback immediately? >> So I'd like to share our perspective which is, you know, nobody spends money for the sake of spending money on technology. It's got to have meaningful business value. So the conversation starts with really renewal and a path to the cloud, but there's a natural opportunity for savings and consolidation that we take advantage. We're not simply shifting from your hardware to the cloud. We're actually modernizing, which will result in significant savings. But it also gives the business something that they don't have today at a level of security and scalability. An ability to run modern technology much faster, much better, and much more scalable. >> Jeff could you give us a sense as to how far you're into this elevate journey maybe thinking about a couple of customers either specifically or generically, you know, where you're at with them, how far along, maybe even some examples that you feel are representative. >> Sure, you know, the, the relationship has been probably about six, close to seven months of maturity. In that time we've had an opportunity to work on several key clients at scale. We've worked together in collaboration on one of the nation's largest retailers in the grocery business. We've worked collaboratively in aerospace and defense, And also in the hospitality industry. In these cases, what we're finding, and one is, each one is in a various stage of maturity. One is done, one is in midstream, and one is at the early stages. And current economic conditions are driving a huge pipeline right now. I think our challenge right now is making sure that we identify those clients that can best take advantage of our services and our joint offering to deal with that pipeline right now. What we're finding is that the savings are at least as we've projected, in some cases we're finding even more. What people say they have and what people say they do isn't necessarily what you find when you get in there. But almost every case, we're finding that there's unused equipment, unused capacity that they currently have, redundancy, low utilization of their current assets. We can go a long way in streamlining that, plus, I can't emphasize enough that, these days, security is a major concern. And we're adding a layer of security that they could never achieve themselves. I'll start off by saying each deal is really custom built around what a customer really needs, what they're trying to get out of it. Right now, as an example, OPEX is very important. So we're engineering deals in a way that helps customers deal with their financial challenges, especially around OPEX. There are other structures that we can put in place. We have the backing of Oracle finance, so we can be very innovative on deals. They can be when value is attained, they can be milestone based. There's just, I think, a wide variety, I don't want to say unlimited, but a wide variety of different options that we can offer our clients in order to be able to deal with whatever financial challenge or opportunity they may be looking at. >> What does success look like, you know, when you were, you know, just less than a year in. When you're two, three, four, let's say five years in and you look back, what does success look like, Aaron? >> So to me success will, success is going to look like we've gotten a number of these big transformation deals in play, it's in motion naturally between our organizations, not necessarily driven entirely by Jeff and I going out and driving the organizations to behave the right way, it's more in our DNA. But more importantly, I think we've gone into, we've gone beyond the conversation of let's move workloads and we've gone into conversations of let's really talk about how to reimagine your business on top of Oracle's cloud and have an ongoing dialogue that looks at that transformation. Once we hit that point, three, four, five years from now, that'll be a wild success in my book. >> Jeff? Final thoughts. >> Deloitte's been around for 175 years, this is our birthday, this year. And in that time what we've learned is there's no substitute for impact and value added to our clients. In our perspective, what success looks like is client's success, client's success means improved scalability of their operations, securing their technology and their data at a substantially lower cost, so that they can focus on what their core business is and focus less on technology. That's success to Deloitte. >> Great Guys thanks so much, great session. We're not only witnessing the rebirth of Oracle consulting, but there's clearly a transformation going on and it's cultural. Gentlemen, congratulations on your partnership and thanks so much for coming in theCUBE. >> Thank you so much. >> Thanks for having us.

>>Yeah, yeah, yeah! >>Everybody, welcome back to the special digital presentation where we are tracking the transformation of Oracle consulting. And really, it's rebirth. Aaron Millstone is back. He's the senior vice president of consulting, joined by Jeff Davis. Who's ah, principal at Deloitte. He's the chief commercial officer for Oracle at Deloitte. Gentlemen, good to see you. Welcome. >>Thank you very much. >>Thanks for having me back. >>You're welcome, guys. Jeff, let me start with you. I've got the obvious question is why would Deloitte World Class? Yes, I well known why you partnering with Oracle Consultant? >>We're really It was a perfect match. The fact that we were looking to grow our oracle practice and really new and innovative ways around Oracle's cloud technology. Uh, in discussions with the oil, coal and specifically with Aaron Millstone, we discovered that we really had complementary capabilities and very little overlapping capabilities. So it was natural for us to find a way to work together. And specifically we found that there were strategic assets we had and there were tactical assets that Oracle had the mixture of two made a really unique and compelling value proposition for the customer base >>and Aaron. I mean, we've talked about the shift from from staff augmentation to much more strategic partnering with your customers. But you're not trying to compete with the big size of there's, there's it sounds like there's not a lot of overlap there. Where do you pick up and leave off for Deloitte? You describe that? >>Sure. I mean, we're You're right, right? We're not. We're not ever going to try to compete with the Deloitte. It's not our that's not in our DNA. It's not our intention. We exist to drive Oracle's to drive success for our customers on Oracle's cloud. That's that's our mantra. That's what we focus in on. So for us, right, we're deep technologists. We're We understand our cloud. We understand how cloud works within our various product suites that we migrate to the cloud. We understand how to manage it. We understand how to build paths extensions to it, but we don't have big program management. We don't understand non oracle components that well, you know, we've got some expertise here and there. But if we need to expand, you know, on Oracle solution to coexist with a Microsoft azure solution, we can't do that without going to a partner and as we bigger and the transformation that they're gonna have to change management and big, big transformation journey capabilities. Like again, That's not That's not expertise. >>Yeah, so Jeff will come back to you. So we see a lot of these deals. Sometimes we call them Barney deals. I love you. You love me. There's a press release, and that's it. But so one of the things we look for okay is their teeth behind this. You guys have come out with what you call elevate. What is elevate? How did it get started? And I have some follow up questions. >>Yeah, well, elevate really got started when Aaron and I started to look at the assets that each of the firms possessed on the Deloitte side, as Aaron suggested, We have deep capabilities and a broad range of technologies, some of them competing technologies with Oracle at the same time. Uh, we didn't have a great deal of depth in Oracle's technical products, Oracle Cloud Infrastructure and Oracle Autonomous. Our bench was not as big as Aaron's, and Aaron also had access to your local development at a level that we didn't have access to. So we really found ourselves in a situation where we could put those two capabilities together and we could offer something to our clients and a broad range of customers. Oracle customers in the field. They had access to all of the Lloyds capabilities, which includes great project management, great change management, real skill around the strategic aspects of cloud migration. And Aaron had tools on had resource is trained and developed around the late historical technology. They'd always be a step ahead of any s I So together we felt this was really a differentiation for marketplace, right, Erin? >>Yeah, absolutely right. And if I don't think I would add to it is that if you if you look at Deloitte approaches client conversation from, ah, business value perspective, you know, the work consulting teams tends to focus conversation. It tends to approach conversations with a focus on How do you want to do the technology? Um, both are helpful. But, you know, quite frankly, as we get into the bigger information in place, we need to lead with the Lloyd model of how do we How do we drive your business value and then begin from a technologist perspective, that's when we show up. So it really has been a very logical, very complimentary match. >>So you and I have talked about, you know, data centers and building data centers and investing. It's not just it's just not a good use of capital today. There's so many other things that organizations can do. You guys have identified data center. Consolidation is, is I'll call it Ah, you know, an initiative that you're seeing customers. I wonder if you could talk about that a little bit. Is that kind of a starting point for conversations? >>Yeah, well, it's definitely starting point, right? So we call it a referred to his infrastructure led transformation, Um, and appetite. The appetite for that is certainly high. We were seeing an increased focus on um, you know what customers need to do to take not just a workload here and there. But how do they get out of the data center business full? So it's a foregone conclusion, right? Like you just said, it's not. It's not really a question of should we invest in another data center? Where should we invest in up to in their data centers? The question has changed to Let's move the cloud. How do we get there and let's move in a big way? And that's why we're seeing that dialogue across all of our customers. And we find even for Oracle, it's been a learning for us, right? We started with on Oracle workload conversation, which is, Do you want to move this work? Work loads of oracle? But you want to move that Oracle workload works. And really, what we're finding is it's a wholesale transformation of everything in the data center, too. One or more clouds, right again, often often it's a multi cloud strategy, and that's okay. And we, you know, we were having more bigger conversations. The thing that has been really interesting is these conversations have evolved, and especially as we work with our partners at Deloitte, has been that, you know, we think that the combination of our our cloud technology, the consulting services that Oracle Consulting and Deloitte can bring to bear and then Oracle's ability to finance the whole deal makes the very compelling conversations for customers because you can walk in to a CEO to a CFO and say, Look on day one, you can actually have a lower spend that what you have today in your data center and get a cop transformation underway at the same time. >>So I want to come back to that business case and member Jeff, before we do, I want to ask you. So we heard Erin, you know, talking about the catalyst. You know, that sort of infrastructure transformation. But you're in the outcomes business, right in both. The bush has been deployed especially so So what is that North Star that you're seeing with customers? You know, it's not about the tech. They're not starting there. Um, that will often tell you that's kind of the easy part. But then we see tech coming and going, and it's the It's the business process. That's the people issues lining everybody. So what are you seeing is so the outcomes. What's that conversation like with your customers? >>Yeah, well, really, this conversation starts with business leadership. Um, if you think about it, there's a strong value proposition in infrastructure renewal. It's not at the top of mind, but once you start to understand the value that's created, it does raise two ah, high priority. Now, our experiences that virtually every board is looking for the C suite toe have a cloud strategy of some kind. People recognize the value of cloud in, uh in many of our clients and many of Oracle's customers, so the boards are pressing the C suite for a cloud strategy. Among those things are the value that cloud brings, including virtually unlimited scalability. Is is being tested real time now with a lot of current events. So when you see the scalability when you know you need a cloud strategy of some kind, your business advisors impressing you, the value proposition starts well, how do we get there? And what does it take to be successful? Our perspective is that it's it's fair to believe that the cloud will reduce infrastructure. Spend significantly. It's a great opportunity for consolidation. It also adds a layer of security, resiliency and scalability that you simply couldn't do on your own. So it addresses a lot of business needs Aziz well as a number of technical needs that need to be addressed. >>So let's talk a little bit more about that business cases that generally what you're seeing, where it starts is let's take some costs right out, and then Aaron, you and I talked about maybe investing that in the future of it. But is that really the starting point for the vast majority of customers? Let's let's let's cut some costs right away and get a payback immediately. >>So I'd like to share our perspective, which is, you know, nobody spends money for the sake of spending money on technology. It's got to have meaningful business value. So the conversation starts with really renewal and a path to the cloud. But there's a natural opportunity for savings in consolidation that we take advantage. We're not simply shifting from your hardware to the cloud we're actually modernizing, which will result in significant savings. But it also gives the business something that they don't have today at a level of security and scalability and ability to run a modern technology much faster, much better. Ah, and much more scalable. >>So a lot of people might again I go back to these deals. I think of this as a sales play. One of the things we look for is there. Is there any other integration? Are you doing co engineering in this case, maybe not, co engineering But are there tools that you're developing that you're taking to market, that you're actually leveraging? Eric, can you talk about that a little bit? Convinces. That's not just the sales play. >>Yeah, sure. And Jeff alluded to some of this earlier, too, right? So we definitely each had our respective tool. Angry Deloitte's investments in tools, what was built out of data that we have seen used quite a few times now we've been investing in something we call the Oracle soar. You know, our tools are, as you'd imagine, heavily Oracle focus. It's about moving Oracle technology to Oracle Cloud out of data and some of the tools that Deloitte's invested in our focus more comprehensively on holistically, looking at everything in a data center and everything that's across data centers and start to develop a set of facts around this stuff. But in both cases, we actually looked at these things and we said, You know what? If you combine these together, we get a very comprehensive view of what exactly it is, but we're looking at with a customer so we can tell everything from the types of traffic we see in the network to the specific versions of stuff you start to identify whether there's risk associated with having things, not aster on a supporter and get a very conference of you that's based on facts. And so, you know, we took those tools. We combined them together so that we can go into a customer and give a complete end and view from both on Oracle and Delight Perspective. And quite frankly, it doesn't matter whether the Lloyd leads or whether Oracle leads. We've developed these tools together. We're going to market together. And we've even got you know, the templates you'd expect consultancies tohave, right? So when you look at business cases, we've got joint business case templates that we've created together and that we're using actively with customers and therefore then we're refining them, improving them each time we do it. But, you know, we're at a point now where our tools are combined, templates are combined, and we even at this, you know, we're even Jeff in our poll earlier yesterday actually even got a joint Ah, war room that's constantly engaging with different account teams and making sure that we structurally approach things in a consistent way so that we're driving business value and using the tools appropriately. >>You know, I think, um, migration risk is probably one of the most significant factors in a business case. I mean, many don't understand it, but those in I t. And certainly hopefully in the executive office do you understand it? It sounds like that's a part of your tooling, anyway is designed to mitigate that's significant migration risk. When you talk about that a little bit, >>yeah, so we, you know, we approach migration from, you know, we start with the conversation. I'm almost always some type of log of what? The list of applications, what versions of things running they've been maintained by some might department somewhere, right? Or the collective? It's in varying degrees of accuracy is what we find. We don't rely on that. We go in and our our tools, our combined tooling across oracle, Deloitte interrogate the systems. We come back with actual information from the actual systems themselves. And then we started the plan. And so the funny thing is, with the migration, you know, probably 80% of the effort. 90% of the effort is in the planning stages and making sure that we understand exactly what we're moving exactly. When again, we're not. We're not dealing with the edge applications. Typically, we're dealing with the mission critical applications that are supporting the heart of a supply chain or a finance operation. And you can't. You just can't afford the down time that maybe you could afford on something that might be a consumer facing or a little less mission. Critical. So, yeah, we start finding very early and interrogate aggressively with actual data. >>Jeff, can you give us a sense as to how far you're into this elevate journey? May be thinking about a couple of customers either specifically or generically gonna where you're at with them. How far along? Maybe even some examples that you feel are representative. >>Sure. Um, you know, the the relationship has been probably about six Ah, close to seven months of maturity. In that time, we've had an opportunity to work on several key clients at scale. Uh, we've worked together in collaboration with one of the nation's largest retailers in the grocery business. We've worked collaboratively in aerospace and defense and also in the hospitality industry. In these cases, what we're finding and one is each one is in the various stage of maturity. One is done, one is in midstream on one is at the early stages and current economic conditions or driving a huge pipeline. Right now, I think our challenge right now is making sure that we identify those clients that can best take a value, take advantage of our services and our joint offering to deal with that pipeline. Right now, what we're finding is that the savings are at least as we projected. In some cases, we're finding even more. What people say they have and what people say they do isn't necessarily what you find when you get in there. But almost every case we're finding that there's unused equipment, unused capacity that they currently have redundancy, low utilization of their current assets. We can go a long way and streamlining that. Plus, I can't emphasize enough that ah, these days security is a major concern and we're adding a layer of security that they could never achieve themselves with soft. >>How do you guys on how the customers wanna approach the transaction? Is it a Bixby is a T and M. Is it a situation where you participate in some of the some of the savings of the game. How does the pricing work? >>So we have Go >>ahead. Um, I'll start off by saying each deal is really custom built around what a customer really needs, what they're trying to get out of it right now. As an example, Op X is very important. So we're engineering deals in a way that helps customers deal with their financial challenges, especially around op Ex. There are other structures that we can put in place. We have the backing of Oracle Finance, so we can be very innovative on deals they could be. When value was attained. They could be milestone based. There's just, uh, I think, a wide variety I don't want to say unlimited, but a wide variety of different options that we can offer our clients in order to be able to deal with whatever financial challenge or opportunity that may be looking at >>perfect, perfect. And you want >>to add to that >>and everything looking at other than you know, the there are. There are always things that are discovered during a personal project, and so, you know, we we also we do factor and things that allow some flexibility. Right? So even if we have a fixed price deal will include a bucket of ours to deal with, you know, unanticipated changes or even innovation. It doesn't have to be, You know, contingency could be Hey, we want to go out and spend and invest some money on artificial intelligence machine learning analytics over in this space since we've already moved these applications. All right, so we're approaching it again from a very flexible standpoint, and we're just point right. We can we can custom craft. Ah, deal to match what? The clients. Best business outcome. Okay. >>Yeah, that makes sense. That client might see some adjacent opportunity that they want to pursue, and they want that to be covered in the agreement I'm gonna end. Um, if you start with you, Aaron and then Jeff go to you. How? What do you guys see? A success? What does success look like? You know, when you were, you know, just less than a year in when you're 234 let's say five years and you look back, What does success look like? >>So, to me, successful success is gonna look like we've gotten a number of these big transformation deals in play. It's in motion, naturally between our organizations, not necessarily driven entirely by Jeff and I going out and driving the organization behave the right way. It's more in our DNA. But more importantly, I think we've gone into We've gone beyond the conversation of Let's Move workloads. We've gone into conversations off. Let's really talk about how to reimagine your business on top of Oracle's cloud and have an ongoing dialogue that looks at that transformation. Once we hit that 0.345 years from now, right, that will be a wild success, Jeff. >>But really, it's been around for 135 years. This is our birthday, uh, this year and in that time, what we've learned is there's no substitute for impact and value added to our clients. In our perspective, what this would success looks like his client success find success means improved scalability of their operations, uh, securing their technology and their data at a substantially lower cost, so that they can focus on what their core businesses and focus less on technology. That success to deploy >>right guys, thanks so much. Great session We're not only witnessing the rebirth of Oracle Consulting, but there's clearly a transformation going on. And it's cultural. Gentlemen, congratulations on your partnership. And thanks so much for coming on the Cube. >>Thank you so much >>for having us. >>You're welcome. Alright, Keep right there, everybody. We're back with our next guest covering Oracle Consulting North America. This is Dave Vellante with the Cube. Thanks for watching. >>Yeah, Yeah, yeah, yeah, yeah, >>yeah.

(upbeat music) >> Everybody, welcome to this special digital presentation where we're tracking the rebirth of Oracle Consulting. And my name is Dave Vellante, and we're here with Aaron Millstone who's the senior vice president of Oracle Consulting. Aaron, thanks for coming on, good to talk to you. >> Dave, appreciate you having me and I like the introduction of a rebirth of Oracle Consulting. >> Well, it really is I mean, you know, you guys have gone from staff augmentation to being much more of a strategic partner and we're going to talk about that. But I want to start with this theme that you have about empowering the autonomous enterprise. Sounds good, you know, nice little marketing tagline. But give us what's behind that, put some meat on the bone? >> Sure, so you know, what we define as autonomous enterprise is really using artificial intelligence, using machine learning and using it to cognitively understand your actual data and processes you're using for your enterprise. And then really embedding that into everything you're doing as a company, and using it to be both drive optimization and costs, and increasing revenue. And I know that's a lot of kind of consulting speak. So, we tend to think about what we've been talking about in terms of what we call tri-modal IT. This is probably the most exciting space that I've really thought through with my team, as we build up a new consulting business, you pull it out, but this is really about pivoting away from the systems of record and the systems of interaction, and really building up the systems of intelligence capabilities that we see all enterprises needing to invest in heavily, if they're not already investing there already. >> Well, I want to talk about a couple of things there. You know, one is that notion of lowering cost or increasing revenue and you're right people say, Oh, yeah, that's consultancy people, but a good consultant digs in and starts peeling the onion. Well, how do you actually make money? You know, where are the inefficiencies in your business? And that's really what you're talking about, and that's what every business wants to know, right? That's the end game, but the how to is really what separates the good consultants from the pack. >> Right right. And we're, you know, again, we're on this journey now, we've been, you mentioned it right. I'm two years into Oracle Consulting. Myself, I spent 23 plus years at Accenture, where I was a managing director with them and part of their North American leadership team. When I came over to Oracle consulting we did, we pivoted from what you called staff augmentation business to a basic set of offerings, which were things that you recognize right migration, services of workloads to cloud or integration or security work or even, step paths for SAS augmentation that we would do, but you know, pretty basic services. We're now pivoting again into sort of two areas infrastructure and transformation, which is really our bold costs take out play, as you just said, and sort of good consultants know how to do that. And really what that is, is we're going and looking at companies that still have traditional data centers, or maybe they've got some things on clouds, and something's still in traditional data centers. And we're coming in, and we're saying there's a business case here, that looks at your total cost of ownership. And we think we can take out between 40 and 65% of your run rate costs, and that's everything from, facilities, fire suppression systems, through to the actual compute cost, through to the labor that's required to do the physical hands on activities in the data center. So, we have that sort of capability and we're pushing customers hard in that space at the moment, and then driving that into a secondary conversation system and by the way, with all these savings, you kind of have to choices. You can pocket the savings, obviously or we would propose that you go into what we're calling the autonomous enterprise space, and really building up your artificial intelligence machine learning capability with centralized capabilities, centralized data, versus letting every line of business, every department do it on their own. >> Now, the other thing a good consultant does is they make the initiative self funding, and that's a win win you keep getting paid, the customer makes money, that's a good thing. But I like the idea, you're starting with the obvious business case of cost, and I think I heard you really attacking OPPEX, labor is obviously big component of that, but it's not just labor, and then you transition if they don't pocket the gain to a gain sharing going forward to look for new revenue. Did I get that I get that right? >> Yeah, you actually got that right. And actually, what I'll tell you too, is I think the labor piece, again, you know, I came from Accenture, Accenture is big outsourcing company, big technology consulting, big strategy consulting. You know, I went in for years and did pitches on outsourcing arrangements which were fundamentally lower cost bodies running in a more effective way. What we're finding or what I'm finding with customer conversations over the last two years at Oracle has been actually I think, data centers are not, there's nothing competitively advantageous about having a data center if you're a company, there is a lot of advantageous. There's an advantage to having cloud and what we're seeing is that companies that might have outsourced their data center are just the lowest cost provider are now considering insourcing or co-sourcing as they pivot the cloud. So the funny thing is actually labor savings is not the big driver of that 40 to 65%, that plays a role of course, that's how you get to the 65%. But even go into the 40% you can get there by insourcing your labor and bringing them in house and recognizing that the speed at which you can operate on your cloud gives you a competitive advantage. >> So this requires a whole new skill set for Oracle, you mentioned, you came in from Accenture where I talked to another number of other folks in Oracle's North America Consulting Operation that came from, brand name firms, we're going to be talking to Deloitte we have and will continue. So there I know, a big part of you talk about the skills transformation that you've affected inside of Oracle Consulting. >> Sure, yeah I mean, it started when I showed up. It was primarily a staff augmentation business in our commercial space in particular, you know, if you need a DBA, here's a DBA. If you need a SAS admin, here's a SAS admin. Here's the hourly rates and quite frankly, very, very talented group of people, very talented, but focused on doing, you know, sort of nuts and bolts level work, very deep work on the Oracle technology stack, but also weren't particularly cloud certified. So we started by focusing on getting the team certified in our cloud products, invested a ton of hours, thousands and thousands of hours in training. It takes you know, we're doing something like six months investment initially to get people up and certified on multiple cloud products that Oracle is selling. And then right from there, we started putting together our basic offerings, again moved from staff augmentation to saying, look, would you like to move a workload. To move a workload is going to cost a fixed price, whatever that is 100, $200,000 move away from rate card conversations with augmentation. And we shifted the commercial contracts that had payments based on outcomes so they don't move successfully, there's no payment. And so you know that was really the focus. >> I'm going to come back to this notion of gain sharing and particularly focus on the revenue side for a moment. You mentioned a what I'll call a buzzword tri-modal IT and a buzzword because Gartner kind of with bimodal IT popularized that concept. And I think part of the problem that people had with bimodal IT was kind of had the legacy systems of record and then you had all the new cool stuff, the big data and you know now AI and systems of engagement and so forth. And everybody wanted to go to the ladder and run away from the former. But now, if I understand tri-model IT, you're talking about bringing machine intelligence to both of those spheres such that people can stay current, stay relevant and add new value to their organization. >> Yeah, that's exactly it. And we're trying to bring it to both but we're trying to make it its own sphere, independent of the other two. So, again, as we looked at this consulting evolution, I didn't come over to Oracle and Oracle is not interested in us, creating a consulting business, that's a me too consulting business that kind of looks like whatever everyone else is doing. So the goal really was okay. So if we started with sort of staff augmentation, and you know, really Oracle's legacy, a system of record stuff, we sell big back office systems, we have mission critical databases. Like it's the clunky stuff that has to work, but really at the end of the day, that's our heritage, going over to the systems of interaction which is, where the bimodal IT really came in from Gartner. That's a pretty saturated place, so again, coming from the background, I had a consulting, I looked at all the eight design agencies that were out there that were all selling digital, and we looked at the digital sales tactics going on, we're like, well, that's pretty saturated, it's not really a smart place for us to go make a lot of headway into. And so we looked and said, well really, the next layer, the next evolution of IT is this third sphere systems of intelligence. And really, since Oracle is, our heritage is mission critical and data, fundamentally, the logical step for us was to go okay, systems intelligence are powered by data, and they serve artificial intelligence as the primary consumer. So again, our thought process was you have a system of record which is process centric and really geared towards the CFO or a head of HR, you have systems of interaction, which is really geared towards the users, it's trying to make business frictionless. Those users can be consumers, they can be employees, whomever. And then systems intelligence is around artificial intelligence is the primary consumer of it. I mean really pivoting to that, and then making that something that is pervasive and structurally place across both those other two spheres, really felt like where we should be differentiating. When I brought in the talent rate that we looked to bring in, we were getting kind of affirmation that, yeah, the best talent in the market was starting to see this trend and so we kind of knew we were onto something there. >> Yeah, I mean, that makes a lot of sense, because as you as you point out, some of those new workloads, many of them are very consumer oriented, that's kind of you know, not your wheelhouse. I mean, that's your customers are, selling to consumers, but Oracle's B2B, hardcore data mission critical. But let me ask you, to that make sets, but by your cloud, you were sort of a later entrant into cloud. So where does cloud fit into this? How do you respond to when customers say, yeah, but you know, you guys were late on the cloud. >> Yeah, we are definitely late coming to cloud, like there's no two ways about it. I mean, what we've got is we have what we call a Generation 2 Cloud. And I jokingly tell customers that we have a late mover advantage. And that late mover advantage basically means that we've looked at what the first generation clouds have done. And quite frankly, they're great at what they do, they're fierce competitors, they're tough to compete with, they've got a lot of mindshare, but they fundamentally were about targeting consumers, or targeting enterprise collaboration tools, so if you want cat videos, if you want to watch humorous videos that people filmed and posted on social media, those are great clouds for that stuff. But if you want really mission critical enterprise cloud workloads, that's where we come into play. And so when you start to look at really the key differentiators in our cloud and through out, at least this is how I describe it to customers. So, we look at sort of three layers, we have an autonomous capability both on our operating system and our database. What that basically means is that we have machine learning and artificial intelligence that's driving the key, administrative activities in our cloud, we then have our Exadata platform. So Exadata for us is a secret weapon, we think that it is a differentiator in our products. And so, Exadata for those watching that doesn't know what it is, so Exadata emerged out of the Sun acquisition that Oracle did. It is purpose built hardware that is engineered for our software products, specifically our databases. And now we've taken that concept and moved it into our cloud and so customers can come in and take very intensive enterprise, mission critical workloads, run them straight in our cloud. And then, when we look at the last point, it's probably security where, again, we have total segmentation of our security layers from the customer workloads. So again, we've taken the concepts that first generation cloud providers have implemented, and they've scaled it globally. So it's really tough for them to walk back on it, it's a huge investment and we're now gone into a Generation 2 Cloud and quite frankly, I think that's what this is the frontier that everyone's racing to kind of grab. >> You know, we actually in our community, talk to a lot of Exadata customers and they get very intense, they do some really hardcore things with with Exadata. To me, the key to your cloud strategy, and specifically Exadata is you've got the same exact infrastructure, control plane, data plane, software, either on prem or in the cloud. So that's your same same narrative. But the real key, new key anyway is what autonomous, tell me if you agree with this. What autonomous gives you a scale, because as you say, you're related to cloud, you're not a hyper scalar in that sense, you're not selling just, race to the bottom infrastructure as a service. You're bringing applications and mission critical applications, so eponymous gives you the ability to scale and compete more effectively with some of those other, earlier movers. You buy that? >> Yeah, absolutely. So scaling and scaling in terms of, what has been historically human activities, when I say human activities, we're not replacing the humans, we're making some of the human activities that were highly repetitive way more efficient. So easy example I can give you is patching. Like security(mumbles) bases are very time consuming, I've talked to customers as recently as a couple weeks ago, that are three years behind on their patching. And when I look at that, it's you're like, why wouldn't you consider autonomous, they have their board of directors and their auditors are actually now demanding that they do something different about their patching problems. And they're talking about, man months, people months of trying to roll out this patching, and they're worried about breaking stuff, and they're worried about human error. Like when you look at something like autonomous, that patching would take place, pretty much instantaneous with no downtime. And we've seen it in our own cloud and our own services internally and we're able to patch, thousands and thousands of cores very, very quickly. >> So we got to wrap but I wanted to close on sort of the, I mean, again, we talked about good consultants and good consultants have continuous improvement mindset. They got a North star that they really never get through and that keeps moving because you got to keep innovating, you got to keep disrupting yourself, so maybe you could end by sort of talking about some of the things you're watching, some of the milestones you want to hit and some of that transformation that you want to keep going. How are you going to achieve that? >> Yeah, and it will skip some of it, when we hit the Deloitte segment too, but like we're definitely we've moved from, we've definitely move from the staff augmentation to basic offerings. We're now beyond that we're starting to sell the infrastructure lead transformation plays. What's exciting to me about that with our customers is, you know, Oracle's a big complex enterprise, as you'd expect with a company that has a tremendous amount of technology. We're now bringing holistic approaches to our customer say, let us help you optimize everything end to end, let's look at your data center, let's not look at a narrow slice, let's not look at just SAS admins and DBAs, we're looking at things comprehensively. So moving there has been a pretty big milestone for us to hit, we've started to get some good momentum with our customers. Our next milestone is really going to be taking that autonomous enterprise and blowing it out. We're in use case and incubation period right now with that, but again we've got some, I would argue we have the best talent in the world right now that thinks about this stuff and not just thinks about it from a pure technology standpoint, but thinks about how to actually make it effective for the business. And so once we get some of those motions going, like the use case for the autonomous enterprise that's artificial intelligence driven, it should have a continuous pace of change, and it's going to start to evolve in areas that you know, quite frankly, we can't even predict yet. But we're excited to see where it leads. >> Alright, thanks for spending some time with us. I am very excited to talk about that sort of collision course between your deep tech capabilities as Oracle as a product company and this, the Global SI, Deloitte, we're going to bring in those guys in a moment. So thanks very much for taking us through the transformation and great job, good luck. >> Thank you, appreciate it. >> All right, and thank you, everybody for watching. Keep right there, we'll be back with more coverage of Oracle's transformation. Right after the short break, you're watching the CUBE. (upbeat music)

>> From Cambridge, Massachusetts, it's theCUBE covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. (dramatic music) >> Welcome back to Cambridge, Massachusetts, everybody. This is theCUBE, the leader in live tech coverage. We go out to the events, and we extract the signal from then noise. And, we're here at the MIT CDOIQ, the Chief Data Officer conference. I'm Dave Vellante with my cohost Paul Gillin. Day two of our wall to wall coverage. Aaron Kalb is here. He's the cofounder and chief data officer of Alation. Aaron, thanks for making the time to come on. >> Thanks so much Dave and Paul for having me. >> You're welcome. So, words matter, you know, and we've been talking about data, and big data, and the three Vs, and data is the new oil, and all this stuff. You gave a talk this week about, you know, "We're maybe not talking the right language "when it comes to data." What did you mean by all that? >> Absolutely, so I get a little bit frustrated by some of these cliques we hear at conference after conference, and the one I, sort of, took aim at in this talk is, data is the new oil. I think what people want to invoke with that is to say, in the same way that oil powered the industrial age, data's powering the information age. Just saying, data's really cool and trendy and important. That's true, but there are a lot of other associations and contexts that people have with data, and some of them don't really apply as, I'm sorry, with oil. And, some of them apply, as well, to data. >> So, is data more valuable than oil? >> Well, I think they're each valuable in different ways, but I think there's a couple issues with the metaphor. One is that data is scarce and dwindling, and part of value comes from the fact that it's so rare. Whereas, the experience with data is that it's so plentiful and abundant, we're almost drowning in it. And so, what I contend is, instead of talking about data as compared to oil, we should talk about data compared to water. And, the idea is, you know, water is very plentiful on the planet, but sometimes, you know, if you have saltwater or contaminated water, you can't drink it. Water is good for different purposes, depending on its form, and so it's all about getting the right data for the right purpose, like water. >> Well, we've certainly, at least in my opinion, fought wars, Paul, over oil. >> And, over water. >> And, certainly, conflicts over water. Do you think we'll be fighting wars over data? Or, are we already? >> No, we might be. One of my favorite talks from the sessions here was a keynote by the CDO for the Department of Defense, who was talking about, you know, the civic duty about transparency but was observing that, actually, more IP addresses from China and Russia are looking at our public datasets than from within the country. So, you know, it's definitely a resource that can be very powerful. >> So, what was the reaction to your premise from the audience. What kind of questions did you get? >> You know, people actually responded very favorably, including some folks from the oil and gas industry, which I was pleased to find. We have a lot of customers in energy, so that was cool. But, what it was nice being here at MIT and just really geeking out about language and linguistics and data with a bunch of CDOs and other people who are, kind of, data intellectuals. >> Right, so if data is not the new oil. >> And, water isn't really a good analogy either, because the supply of water is finite. >> That's true. >> So, what is data? >> Yeah. >> Space? >> Yeah, it's a good point. >> Matter? >> Maybe it is like the universe in that it's always expanding, right, somehow. Right, because any thing, any physic which is on the planet probably won't be growing at that exponential speed. >> So, give us the punchline. >> Well, so I contend that water, while imperfect, is, actually, a really good metaphor that helps for a lot of things. It has properties like the fact that if it's a data quality issue, it flows downstream like pollution in a river. It's the fact that it can come in different forms, useful for different purposes. You might have gray water, right, which is good enough for, you know, irrigation or industrial purposes, but not safe to drink. And so, you rely on metadata to get the data that's in the right form. And, you know, the talk is more fun because you've a lot of visual examples that make this clear. >> Yeah, of course, yeah. >> I actually had one person in the audience say that he used a similar analogy in his own company, so it's fun to trade notes. >> So, chief data officer is a relatively new title for you, is it not? In terms of your role at Alation. >> Yeah, that's right, and the most fun thing about my job is being able to interact with all of the other CDOs and CDAOs at a conference like this. And, it was cool to see. I believe this conference doubled since the last year. Is that right? >> No. >> No, it's up about a hundred, though. >> Right. >> Well. >> And, it's about double from three years ago. >> And, when we first started, in 2013, yeah. >> 130 people, yeah. >> Yeah, it was a very small and intimate event. >> Yeah, here we're outgrowing this building, it seems. >> Yeah, they're kicking us out. >> I think what's interesting is, you know, if we do a little bit of analysis, this is a small data, within our own company, you know, our biggest and most visionary customers typically bought Alation. The buyer champion either was a CDO or they weren't a CDO when they bought the software and have since been promoted to be a CDO. And so, seeing this trend of more and more CDOs cropping up is really exciting for us. And also, just hearing all of the people at the conference saying, two trends we're hearing. A move from, sort of, infrastructure and technology to driving business value, and a move from defense and governance to, sort of, playing offense and doing revenue generation with data. Both of those trends are really exciting for us. >> So, don't hate me for asking this question, because what a lot of companies will do is, they'll give somebody a CDO title, and it's, kind of, a little bit of gimmick, right, to go to market. And, they'll drag you into sales, because I'm sure they do, as a cofounder. But, as well, I know CDOs at tech companies that are actually trying to apply new techniques, figure out how data contributes to their business, how they can cut costs, raise revenue. Do you have an internal role, as well? >> Absolutely, yeah. >> Explain that. >> So, Alation, you know, we're about 250 people, so we're not at the same scale as many of the attendees here. But, we want to learn, you know, from the best, and always apply everything that we learn internally as well. So, obviously, analytics, data science is a huge role in our internal operations. >> And so, what kinds of initiatives are you driving internally? Is it, sort of, cost initiatives, efficiency, innovation? >> Yeah, I think it's all of the above, right. Every single division and both in the, sort of, operational efficiency and cost cutting side as well as figuring out the next big bet to make, can be informed by data. And, our goal was to empower a curious and rational world, and our every decision be based not on the highest paid person's opinion, but on the best evidence possible. And so, you know, the goal of my function is largely to enable that both centrally and within each business unit. >> I want to talk to you about data catalogs a bit because it's a topic close to my heart. I've talked to a lot of data catalog companies over the last couple years, and it seems like, for one thing, the market's very crowded right now. It seems to me. Would you agree there are a lot of options out there? >> Yeah, you know, it's been interesting because when we started it, we were basically the first company to make this technology and to, kind of, use this term, data catalog, in this way. And, it's been validating to see, you know, a lot of big players and other startups even, kind of, coming to that terminology. But, yeah, it has gotten more crowded, and I think our customers who, or our prospects, used to ask us, you know, "What is it that you do? "Explain this catalog metaphor to me," are now saying, "Yeah, catalogs, heard about that." >> It doesn't need to be defined anymore. >> "Which one should I pick? "Why you?" Yeah. >> What distinguished one product from another, you know? What are the major differentiation points? >> Yeah, I think one thing that's interesting is, you know, my talk was about how the metaphors we use shape the way we think. And, I think there's a sense in which, kind of, the history of each company shapes their philosophy and their approach, so we've always been a data catalog company. That's our one product. Some of the other catalog vendors come from ETL background, so they're a lot more focused on technical metadata and infrastructure. Some of the catalog products grew out of governance, and so it's, sort of, governance first, no sorry, defense first and then offense secondary. So, I think that's one of the things, I think, we encourage our prospects to look at, is, kind of, the soul of the company and how that affects their decisions. The other thing is, of course, technology. And, what we at Alation are really excited about, and it's been validating to hear Gartner and others and a lot of the people here, like the GSK keynote speaker yesterday, talking about the importance of comprehensiveness and on taking a behavioral approach, right. We have our Behavioral IO technology that really says, "Let's not look at all the bits and the bytes, "but how are people using the data to drive results?" As our core differentiator. >> Do your customers generally standardize on one data catalog, or might they have multiple catalogs for multiple purposes? >> Yeah, you know, we heard a term more last season, of catalog of catalogs, you know. And, people here can get arbitrarily, you know, meta, meta, meta data, where we like to go there. I think the customers we see most successful tend to have one catalog that serves this function of the single source of reference. Many of our customers will say, you know, that their catalog serves as, sort of, their internal Google for data. Or, the one stop shop where you could find everything. Even though they may have many different sources, Typically you don't want to have siloed catalogs. It makes it harder to find what you're looking for. >> Let's play a little word association with some metaphors. Data lake. (laughter) >> Data lake's another one that I sort of hate. If you think about it, people had data warehouses and didn't love them, but at least, when you put something into a warehouse, you can get it out, right. If you throw something into a lake, you know, there's really no hope you're ever going to find it. It's probably not going to be in great shape, and we're not surprised to find that many folks who invested heavily in data lakes are now having to invest in a layer over it, to make it comprehensible and searchable. >> So, yeah, the lake is where we hide the stolen cars. Data swamp. >> Yeah, I mean, I think if your point is it's worse than lake, it works. But, I think we can do better a lake, right. >> How about data ocean? (laughter) >> You know, out of respect for John Furrier, I'll say it's fantastic. But, to us we think, you know, it isn't really about the size. The more data you have, people think the more data the better. It's actually the more data the worse unless you have a mechanism for finding the little bit of data that is relevant and useful for your task and put it to use. >> And to, want to set up, enter the catalog. So, technically, how does the catalog solve that problem? >> Totally, so if we think about, maybe let's go to the warehouse, for example. But, it works just as well on a data lake in practice. >> Yeah, cool. >> Through the catalog is. It starts with the inventory, you know, what's on every single shelf. But, if you think about what Amazon has done, they have the inventory warehouse in the back, but what you see as a consumer is a simple search interface, where you type in the word of the product you're looking for. And then, you see ranked suggestions for different items, you know, toasters, lamps, whatever, books I want to buy. Same thing for data. I can type in, you know, if I'm at the DOD, you know, information about aircraft, or information about, you know, drug discovery if I'm at GSK. And, I should be able to therefore see all of the different data sets that I have. And, that's true in almost any catalog, that you can do some search over the curated data sets there. With Alation in particular, what I can see is, who's using it, how are they using it, what are they joining it with, what results do they find in that process. And, that can really accelerate the pace of discovery. >> Go ahead. >> I'm sorry, Dave. To what degree can you automate some of that detail, like who's using it and what it's being used for. I mean, doesn't that rely on people curating the catalog? Or, to what degree can you automate that? >> Yeah, so it's a great question. I think, sometimes, there's a sense with AI or ML that it's like the computer is making the decisions or making things up. Which is, obviously, very scary. Usually, the training data comes from humans. So, our goal is to learn from humans in two ways. There's learning from humans where humans explicitly teach you. Somebody goes and says, "This is goal standard data versus this is, "you know, low quality data." And, they do that manually. But, there's also learning implicitly from people. So, in the same way on amazon.com, if I buy one item and then buy another, I'm doing that for my own purposes, but Amazon can do collaborative filtering over all of these trends and say, "You might want to buy this item." We can do a similar thing where we parse the query logs, parse the usage logs and be eye tools, and can basically watch what people are doing for their own purposes. Not to, you know, extra work on top of their job to help us. We can learn from that and make everybody more effective. >> Aaron, is data classification a part of all this? Again, when we started in the industry, data classification was a manual exercise. It's always been a challenge. Certainly, people have applied math to it. You've seen support vector machines and probabilistic latent cement tech indexing being used to classify data. Have we solved that problem, as an industry? Can you automate the classification of data on creation or use at this point in time? >> Well, one thing that came up in a few talks about AI and ML here is, regardless of the algorithm you're using, whether it's, you know, IFH or SVM, or something really modern and exciting that keeps learning. >> Stuff that's been around forever or, it's like you say, some new stuff, right. >> Yeah, you know, actually, I think it was said best by Michael Collins at the DOD, that data is more important than the algorithm because even the best algorithm is useless without really good training data. Plus, the algorithm's, kind of, everyone's got them. So, really often, training data is the limiting reactant in getting really good classification. One thing we try to do at Alation is create an upward spiral where maybe some data is curated manually, and then we can use that as a seed to make some suggestions about how to label other data. And then, it's easier to just do a confirm or deny of a guess than to actually manually label everything. So, then you get more training, get it faster, and it kind of accelerates that way instead of being a big burden. >> So, that's really the advancement in the last five to what, five, six years. Where you're able to use machine intelligence to, sort of, solve that problem as opposed to brute forcing it with some algorithm. Is that fair? >> Yeah, I think that's right, and I think what gets me very excited is when you can have these interactive loops where the human helps the computer, which helps the human. You get, again, this upward spiral. Instead of saying, "We have to have all of this, "you know, manual step done "before we even do the first step," or trying to have an algorithm brute force it without any human intervention. >> It's kind of like notes key mode on write, except it actually works. I'm just kidding to all my ADP friends. All right, Aaron, hey. Thanks very much for coming on theCUBE, but give your last word on the event. I think, is this your first one or no? >> This is our first time here. >> Yeah, okay. So, what are your thoughts? >> I think we'll be back. It's just so exciting to get people who are thinking really big about data but are also practitioners who are solving real business problems. And, just the exchange of ideas and best practices has been really inspiring for me. >> Yeah, that's great. >> Yeah. >> Well, thank you for the support of the event, and thanks for coming on theCUBE. It was great to see you again. >> Thanks Dave, thanks Paul. >> All right, you're welcome. >> Thank you, sir. >> All right, keep it right there, everybody. We'll be back with our next guest right after this short break. You're watching theCUBE from MIT CDOIQ. Be right back. (upbeat music)

>> Announcer: Live from New York. It's the Cube. Covering AWS Global Summit 2019. Brought to you by Amazon Web Services. >> Welcome back rush hour's started a little bit early here in New York City with over 10,000 people in attendance for AWS summit in New York City. I'm Stu Miniman, my co host for today is Corey Quinn. Happy to welcome to the program two first time guests from our host, Amazon Web Services. To my right here is Deepak Singh, who's the Director of Compute Services. Sitting to his right is Aaron Kao, who's the Senior Manager of Product Marketing. Gentlemen, thanks so much for joining us. >> Thank you for having us. >> Thank you for having us. >> Alright, so we know that every day we wake up and there's new announcements coming from Amazon and the only way most of us keep up with it is trying to read Corey's newsletter here. But in your group in compute, we know there's a lot going on and quite a few announcements. So Aaron, why don't you kick us off with some of the hard news that went through this morning? >> Yeah, we just launched Amazon EventBridge. It's a serverless event boss that allows you to connect your applications with data from sources like SaaS applications, AWS resources and your own applications. >> All right, so Deepak, I would love to dig into that a little bit. Like you said you that Amazon, you've learned a lot from CloudWatch and building this tool. Everybody looking at kind of, you know, Lambda in the serverless space is like, Okay, how are all these pieces going to come together? Is it all Amazon services all the time? And of course, Amazon has a huge ecosystem, but help us understand or layer down you know how this works? >> Yeah so as you know, AWS services send events to CloudWatch events. They consume events from CloudWatch events. One of the best ways to do it is through Lambda. One of Lambda's biggest strengths is the number of integrations we have with event sources, both taking in events and triggering events. But to your point, there are always events inside database ecosystem. And I think one of the things as a service owner that really excites me about EventBridge is how now customers have access not just to event triggers inside AWS, but also to our partners like Zendesk and the applications you can build will be really exciting. >> Alright, quite a few other announcements, maybe walk us through some of them. >> Yeah, CDK is another announcement where it's an open source software development framework that allows you to model your applications using programming language like TypeScript, Java, Python and .net. You know, the whole thing with building in the cloud, it's slightly different. You used to take your code, put it on a server and run it. Now people are building things a little more distributed, using a lot of different resources for their applications. So it's getting, provisioning your infrastructure is a little bit harder, right? You either have to do a lot of things manually or maybe you're writing a lot of scripts or using a domain specific language. But with CDK, you're now able to use the programming languages that you're programming your applications with, to model and provision your infrastructure. So it's super helpful. Really think it's going to help developers increase their development velocity. They're able to use things like loops, conditions, object oriented programming, they don't have to do context switching and just with a few lines of code, they're able to do a lot more. >> All right. >> I wound up playing with it a little bit when it was in preview and one of the things that I found that it was extremely helpful was, it was a lot easier for me to write something in using CDK, and then see what that rendered down to in terms of cloud formation and then oh, I guess that's how I do it in cloud formation, which was great. The counterpoint though, is it also felt at times like it was super wordy. So if I read that what it generates compared to what I normally write, which is admittedly awful, but I almost start to feel like I'm doing it wrong with that and then with amplify and with Sam and the rest, there's a lot of higher level abstractions that build cloud formation for you. But then it renders down in a few different and key ways. Under the hood, how much are these products that you're coming out with starting to shape the direction of cloud formation itself, or is that mostly baked and done? >> There's a lot of products that we're building that you know, are complementing cloud formation. You know, cloud formation is the templating modeling language to provision AWS resources. But on top of that, we have things like Sam right, that provides a declarative a more high level abstract declarative way to build on top of cloud formation, you know, we have Amplified that also uses cloud formation to help you build mobile applications and front end development. And then finally, you have CDK for just general use. So, these things are all complementing and, you know, things customers are asking for and helping us shape the ecosystem there. >> Yeah, Deepak the container space, of course, has been you know, one of these tidal waves that we've been watching and it's fundamentally changing the way people architect their applications and has huge impact on your product line. Give us the update. If you could just start with some of the high level, I remember first when I talked to you a couple of years ago it was when the whole Kubernetes piece was sorting out. So you know, ECS, EKS, used to have a much longer name that Cory would constantly >> Only for Cory >> Finally you've fixed the compensation problem where someone was getting compensated based upon number of syllables and a service name so good on you on that one. >> Right and you know the acronym A-M-I maybe you can you know settle once and for all you know how how we pronounce that. >> I'm old school it'll always be AMI. (laughs loudly) >> Walk us through kind of, you know your container services. >> I think the great thing about containers is as you said the adoption is everywhere. And what we find is there's a growth of ECS, the growth of EKS whether you're running it on EC2 or Fargate everything is growing like crazy, because people find new interesting ways to run applications based on what they know and what they're comfortable with. We have customers, customers like SNAP that know Kubernetes well and they are building on there're building a big chunk of their new infrastructure on EKS on AWS and it basically helps the developer velocity. On the flip side, you have customers like Turner Broadcasting that run a lot of their web services or the Comedy Central content properties like that on Fargate because they can just stamp them out. They all you know, it's a website, it's a service that they can just keep expanding. So it boils down to what are the key things that you're comfortable with? What are the reasons you've picked something. So if you're running like SNAP across, you know, in many different places, you are likely to choose Kubernetes and standardize on that. So that's the best part for me is, people have choices and then they pick based on what they need at that point in time, which can be two different teams at the same place, picking a different solution. I will add that one of the areas that we are focused on now is observe ability and developer experience. Those are areas that our customers have been asking for. CDK plays into that you saw in the demo this morning and with observe ability with container insights and with the fluid plugins that we announced. I think those are areas that you'll see us do a lot more going forward. >> So right, that was one of news today, CloudWatch container insights just to explain what that one is. >> So historically, when you do CloudWatch look, it's very BM-centric, you're looking at CPU memory, you assuming an application, instances run for a particular period of time. In the container world, you have services where the underlying tasks come and go, all you know, at a very different rate. CloudWatch container insights is meant to be a world that's aware of the fact that your containerized applications are tasks and services and pods, so you're able to get more fine grained metrics on the things that container customers care about and you're not trying to use BM-centric language to look at a containerized infrastructure. So that's the biggest reason for doing that. And then on the Fluent Bit side was, our customers want log routing to whatever they want to do it on. Whether they want it to send to S3 or the Elasticsearch We do that with Kinesis Data Firehose. So we basically wrote a bunch of open source plugins for Fluent Bit that just send your logs where you want them to go. So that's kind of where we are focused. >> Yeah, I view it as more of a log router than I do almost anything else. >> It is that. >> Yeah. A question of: Where does it come from? Where does it go? How do you keep it straight? >> Yeah. >> It's at this point, what does it output to you these days? Are there are various destination options, third party vendors, CloudWatch, history? >> So we wrote two plugins one was for well three, I don't know. One for S3 because so many people don't understand the data to S3. The other one was a Kinesis Data Firehose. So from there, you can send it to Redshift, you can send it to you can send it to Elasticsearch. So based on what you however you want another analyze it, you can send it to a custom resource that's Kinesis. So, you're using some third party provider, you can just send your logs over to those. >> Yeah, Corey, you know, you're dealing with a lot of customers, you know, there's now so many, you know, different instance types and some of the pieces, you know, what's the feedback you're giving to, you know, Amazon these days? >> Entirely depends upon the service teams and it ranges from this is amazing, excellent job to okay, it's a good start. And it's always a question though, it's when you have what 200 service options or darn near it at this point, 170. It's impossible to wind up with something that is evenly consistent and you have services that are sub components of other services and built on top. I mean, I think the, I guess the feedback I've been giving almost universally across the board is, assume that I am about 20% as smart as you right now seem to think I am and then explain it to me and then I'll probably understand it a lot better. It comes down to service to storytelling, more or less of meeting people at various points along their journey and then I was mentioning in our editorial session just before this segment, that that's something that AWS has markedly improved on the last two or three years. Where you have customer stories that are rapidly moving up the stack as far as leverage services. It's not just we took the VMs and now we run them somewhere else. Now it's about building a high, extremely volume intensive applications on top of a whole bunch of managed services and these are serious companies. These are regulators it's not just Twitter for pets anymore. >> Nothing wrong with that. >> No. >> So, you know, we were discussing, like FINRA was a great case study this morning and they talked about in the four years that they've been on, they've re-architected three times. You know, how do you balance all of these new instances coming out with, you know, and how do I make sure that I deploy something today that I've got the flexibility to change, but you know, I want to be able to lock in my pricing and make it easier. >> So actually, we think about that quite a bit. One of the reasons we built app match the way we did, as something that sits outside the container orchestrator, was it doesn't lock you into choosing one or the other or even choosing an architecture. You can start off with a monolith, start putting side cards on it, getting visibility into all your traffic, then portions of your applications you can start breaking out, you can put them on Fargate, you can put them on ECS, you can put them on the EC2. I think that is something we did very consciously because so many of our customers are in that position and I think more and more are going to go higher up the stack using managed databases, using Lambda, but it's not decision they need to make all up front. They can do it piecemeal, and we see our customers find another good example, they've done that. >> One of the philosophies of it, like AWS is giving customers building blocks to build things on. So the whole thing is, here's a new primitive that you can use, then you can take it out, replace something with something else, depending on your needs. So we give customers flexibility and choice. >> And part of the problem is that, that very much becomes a double-edged sword. I mean, most recently, you've had effectively declared war on Alphabet. I don't mean the large cloud provider that turns things off for a living. I'm talking about the English alphabet, where you take a look at all the different EC2 instance types. I think in US East one now there's over what is it 190 different instances you can pick from. It leads to analysis paralysis, which one do I pick? What's the right answer? What am I committing to, what am I not? And you see, that's a microcosm of the larger service problem. I want to build a web app that does a thing, which services do I use, you open up the service listing and you just get this sort of sinking sensation? I get that I can't imagine what someone new to the space is getting to there. >> All right, and this is where things like Amplify, Fargate, AWS Batch where you don't need to select an instance. Where you just tell us what your requirements are and Batch makes that selection for you. The core building blocks are important because you can't really figure out what to do. But then you'll see us do much more about the stack to help people get there. It's an ongoing thing that will keep trying to tackle but you'll see a lot more of that. >> It's controversial. One of my favorite things about Lambda, for example, is there's one knob RAM and as you turn that up, other performance characteristics increase and people complain about it but I love the simplicity, because I don't have to sit and think and make all these different decisions. It's one access. >> Yeah, but if you want more knobs, you can use Fargate. So I think that, that's the beauty of it that you do have that choice. >> Yeah, one of the lines Aaron, I really liked in Werner's keynote is he said, "we've really, you know, my words commoditized IT. "We all have access to all of the tools now." You know, that was, you know what big data originally and cloud also was, you know, you used to have to be a nation state or fortune 100 to be able to do some of these things so, you know, what do you hear from customers? You know, how do they make sure, you know, they're staying competitive and ahead, and therefore, in that relationship between the business and IT, what do you hear from your customers these days? >> In terms of that? Well, I think for, you know, for customers, like I think EventBridge is a, a pretty good example of that, in terms of customers asking us for ability to, you know, integrate their SaaS providers, integrate a lot of different things and not have to, you know, not have to do a lot of undifferentiated heavy lifting and things like that and, you know, customers are increasingly moving towards like event driven architectures and they asked us, hey, we really like CloudWatch events and how you do things with IT automation and then bringing SaaS providers in and, we want to, you know, we don't want to build pulling infrastructure in order to access API's and do all all those heavy liftings. What we did was we built out, we took CloudWatch events and added new features for SaaS applications and built that into a separate service for people to use. So that's like, you know, a lot of the relationships we have with our customers, listening to what they need and giving them what they want. >> And I think that, that's a very valuable thing. You know, we used to say, you know, five years ago, you would talk about, you know, let's get rid of undifferentiated heavy lifting. >> Yeah. >> Well, now it's like, no, no, let's enable, you know, something that you would have thought was heavy lifting and we're daunted to be able to do it but now hopefully, it's easier, because a lot of this stuff, you know, as Corey said, this is still a little bit daunting and you know, well you've got a lot of ecosystem and service providers and services to help us, you know, take care of, you know, because it's the Paradox of Choice with all the options that you have. >> And I think that's the beauty of what, I mean our customers are smart, they manage to find it interesting ways to keep challenging us and they keep us busy. But I also think that really, really many of them, the ones who have been able to be successful, have figured out what it means to take all the tools we give them, which are the ones where they want to completely hand it over to AWS and give us the responsibility and then which ones do they really feel they care about and the ones who can find their balance are the ones that we see moving the fastest. I think that's what we're trying to do. >> All right, now and one thing that does absolutely permeates virtually every service team I've worked with at AWS, I mean, you I've had this experience with you, where I talked about how my use case isn't a terrific fit for your product and your response is always well, what is your use case? It's not, is starting off from the baseline assumption that my use case is ridiculous, which let's face it, it probably is. But being able to address a customer need and understand that even if it doesn't dictate roadmap, is incredibly valuable and I don't find that there are too many players in any space, let alone this one that are willing to have the patience to listen to, frankly, some loud person wearing a suit. >> We try, I mean, I think you heard Andy say there's so much like a big chunk 85, 90% of our roadmap is customer requests, I would say that even the remaining 10% is maybe not things that they've directly asked for but things that we've observed they've run into or that we've run into working with, you know, the one or two customers who are ahead of the pack. And Okay, they have this problem, how do you generalize that? And we try and understand what it means. One of the reasons we made the container roadmap public, was this space is moving so quickly, it's almost impossible for us to talk to enough customers to figure that out. So like, Okay, this gives us an avenue for them to come to us and just tell us, GitHub issues. >> Yeah, so right. Final question I have for both of you. Directionally looking forward, you know, the roadmap, we love when there is publicly facing material not under the NDAs that we normally have to be able to hear. So what are you hearing from your customers? What direction are they pulling you towards and that we should expect to watch AWS kind of further, as we head towards re:Invent later this year. >> I think customers are asking us for different things for developer experience, especially event driven architectures. I think there's going to be a lot of interesting things happening in the Lambda space and that entire space. >> Yeah and to add to that, I think, to your point earlier, helping them simplify choices is going to be a big part of it. Meeting them where they are, in their IDEs with a tooling is a big part of what you'll see us do. So, you know, I think you saw examples today and we'll keep building on top of those. >> All right, well, send our congratulations to the two pizza teams that worked on all of the projects that were announced today. Look forward to seeing you, you know, down the road. Thanks so much and welcome to being Cube alumni. >> Thank you for have us. >> Thank you for having us on. >> Appreciate it. >> Aaron, Deepak you know, from AWS. He's Corey Quinn, I'm Stu Miniman. Back with lots more coverage from AWS summit, here in New York City, thanks for watching the Cube.

>> live from New York. It's the Q covering AWS Global Summit 2019 brought to you by Amazon Web service, is >> Welcome back. Rush hours started a little bit early here in New York City, with over 10,000 people in attendance for any of US Summit in New York City. I'm stupid, and my co host for today is Corey Quinn. Having a welcome to the program to first time guests from our host Amazon Web service is to my right. Here is Deepak Singh, who is the director of Compute Service's. To his right is Aaron Cow, Who's the senior manager product marketing Gentlemen, thanks so much for joining us. Thank >> you for having us >> for having us, all right, so we know that every day we wake up and there's new announcements coming from Amazon, and the only way most of us keep up with it is trying to re Cory's newsletter here. But in your group and computer, we know there's a lot going on and quite a few announcements. So, Aaron, what do you kick us off with? Some of the hard news that went >> through this morning? Yeah, we just launched Amazon event. Bridgette's Ah, serverless event boss that allows youto connect your applications with data from sources like sass applications. A devious resource is in your own applications. >> All right, So Deepak would look to dig into that a little bit. I like you said, you that Amazon. You learned a lot from cloudwatch in building this tool. Everybody looking at kind of lambda and the service faces, Like Okay, how all these pieces together is that all? Amazon service is all the time. And, of course, Amazon has a huge ecosystem. But help help us understand a layer down. You know how this works. >> Yeah. So, you know, a dress service send events watchman consumer event from one of the best ways to do it is through Lando. Lando. One of London's biggest trends is the number off integration we have with events both taking in events and triggering event. But to your point there already events inside database system. I think one of the things as a service owner, that really excites me about event. How now? Customers of access, not just two ventricles inside eight of us were awesome apartments extended so that the application you can build will be really exciting. >> Quite a few other announcements maybe August or someone CK >> is another announcement where it's open. Source. Software development framework allows you to model your applications using programming language like typescript Job a python and got that. You know the whole thing with building in the cloud. It's slightly different. You usedto take your coat. Put it on a servant. Run it. Now people are building things a little more distributed. Using a lot of different resource is for their applications, so it's getting provisioning. Your infrastructure is a little bit harder, right? Either Have to do a lot of things manually. Are maybe you're writing. A lot of scripts are using a domain specific language, But with CD Kay, you're now able to use the programming languages that you're hurting your applications with two model and provisions your infrastructure. So it's super helpful. Really think it's gonna help developers increase their development velocity? They're able to use things like loops, conditions, object oriented programming. They don't have to do context switching and just a few lines of code. They're able to do a lot more. All right, >> I want I want a playing with it a little bit when it was in review, and one of things that I found that it was extremely helpful was it was a lot easier for me to write something in using CD kay and then see what that rendered down to in terms of cloud formation. And then, oh, I guess that's how I do it in cloud formation, which was great. The counterpoint, though, is it also felt, at times like it was super wordy. So if I read that what it generates compared to what I normally right, which is admittedly awful. But it's all right, we'll start to feel like I'm doing it wrong with that. And then with amplify and with Sam and the rest. There's a lot of higher level abstractions that build cloud formation for you. But then it renders down in a few different key ways under the hood. How much are these products that you're coming out with starting to shape the direction of confirmation itself? Or is that mostly baked and done? >> There's a lot of products that we're building that you know are complimenting information. Information is the template ing modeling language to provisional abusive resource is put on top of that. We have things like Sam, right? That provides a declared of ATM or high level abstracted declared way to build on topical information. You know, we have amplified also use this information to help you build mobile applications in front development and then finally have see decay for general use other things. They're all complimenting and you know are things customers are asking for helping us >> get the ecosystem. Deepak. The container space, of course, has been You know what one of these tidal waves that we've been watching on It's fundamentally changing the way people architect their applications. That huge impact on your product line Give us the update. If you could just start with some of the high level. Remember first when I talk to you. A couple of years ago, the whole kubernetes piece was sorting out. So you know, e c s E. K s usedto have a much longer name that Cory >> Cory. Finally, you fix the compensation problem where someone was getting compensated based upon number of syllables in a service name. So good on you on that one. >> Right on. Uh, you know, acronym, am I? Maybe you can you know, settle once and for all. You know how how we pronounce that >> I'm old school in love with the Army. >> But what what walk us through? Kind of. You know, your container service is, >> I think, the great thing about container, I said, adoption is everywhere on what we find. It brought a VCs the growth of cares where they're running it on to our fargate. Everything is growing like crazy because people find new interesting ways to run applications based on what they know. One what they're comfortable with their customers. Customers like Snap. There's no community well, and they're building on their building a big chunk of their new infrastructure on kneecaps or need to be with, and it basically helped develop a velocity. On the flip side, your customers like Turner Broadcasting that run a lot of their Web service is the comedy central content properties like that on Fargate because they can just stamp them out. They all you know, it's about time. It's a service that you can just keep expanding. So it boils down to one of the key things that you're comfortable with. One of the reasons you fix something if you are running like snap across. You know, in many different looks places you are likely to choose community and standardize on that. So that's the best part for me is people have choices and then the pic based on what they need. At that point in time, it can be two different teams at the same place. Picking a different solution. I will add that one of the areas that we are focused on now is a dub ability and develop experience, though the areas that our customers have been asking for CD Kay played into that record in the demo this morning. And with the probability with container inside on with the fluid that be announced, I think though that area, they do a lot more >> going forward, right? That was one of those cloudwatch container insights. Just explain what that one is >> so historically, when you do cloudwatch look very bm centric, you're looking at CPU memory. You're zooming application. We are instances run for a particular period of time. At the container world you have service is with the underlying tasks. Come and go all you know, a very different rate container inside. It's meant to be a world aware of the fact that you're containerized application that fast service is and part, they're able to get more fine grained metrics on the things that container customers care about. And you're not trying to use the BM centric language to look at the content. That's the biggest reason for doing that. And then on the floor in bedside Boy, our customers want loud rounding to whatever they want to do it on where they understand three or elasticsearch. We do that with data borrows. So we basically wrote a bunch of open source plug in for fluent, but they just end your log where you want them to go. That's kind of maybe a >> Yeah, I view it as more of a log router than I do. Almost anything else? Yeah, a question of where did it come from? Where does it go? How do you do? Keep straight. It's at this point. What is it out? What is it output to these days of their various destination options? Third party vendors cloudwatch history >> to plug in 14315413 because so many people in the center there with three the other one was like Anita. There. Apart from there, you can send it to read, Chef, you can send it todo you can send it to elasticsearch. So based on what however you want and I'll analyze it, you can send it to a custom resource. So you want you're using some third party provider. You can just send your logs over to those. >> Corey, you know, you're dealing with a lot of customers. You know, there's so many, you know, different instance types and some of some of the pieces. You know, what's the feedback you're giving? You know, Amazon these days >> entire depends upon the service teams, and it ranges from This is amazing. Excellent job, too. Okay, it's a good start, and it's always a question, though. It's when you have what 200 service options are darn near. It at this point aren't 70. It's impossible to wind up with something that is evenly consistent, and you have service is that air sub components of other service is built on top. I mean, I think the uh, I guess the feedback I've been giving almost universally across the board is assume that I am about 20% as smart as you right now seem to think I am and then explain it to me, and then I'll probably understand it a lot better. It comes down to service the storytelling more or less of meeting people of various points along their journey, and that I was mentioning in our editorial session just before this segment that that's something that AWS has markedly improved on the last two or three years, where you have customer stories that are rapidly moving up the up the stack as Faras Leverage Service's It's not just we took the EMS, and now we run them somewhere else. Now it's about building of extremely volume intensive applications on top of a whole bunch of managed service is and these air serious cos these air regulators. It's not just Twitter for pets anymore. >> Nothing wrong with that. No, >> So way were discussing like Enron was a great case this morning, and they talked about in the four years that they've been on, they re architected three times, you know, how do you balance all of these new wins is coming out with, you know, how do we make sure that I deploy something today that I've got the flexibility to change. But, you know, I want to be able to lock in my pricing and make it easier. >> Actually, we think about that quite a bit. One of the reasons we met, the way we did something that sits outside a container orchestrator. What? It doesn't lock you into choosing one or the other or even using an architecture. You can start over the monolith, start putting sidecars on it. It's getting with the ability to all your traffic portions of applications. You can start breaking out. You can put them on target. You can put them on PCs. You can put them on it, too. I think that is something we did very consciously because so many of our customers are in that position. And I think more and more are going to go higher up the stock using managed databases. You think lambda. But it's not decision they need to make all up front. They can do it piecemeal, and we see a custom fender. The good example there done that. >> I think one of the >> philosophies of like eight of us is giving customers building blocks the buildings on, so the whole thing is here's a new primitive that you can use. Then you can take it out, replace something with something else, depending on your needs. So we give customers flexibility and choice. >> And part of the problem is that that very much becomes a double edged sword. I mean, most recently you've had effectively declared war on alphabet. I don't mean the large cloud provider that turns things off for a living. I'm talking about the English alphabet where you take a look at all the different ec2 instance types. I think in US East one. Now there's over. What is it? 100 90 different instances you can pick from. It leads to analysis paralysis. Which one do I pick? What's the right answer? What am I committing to? What am I not? And you see that? That's a microcosm. The larger service problem. I want to build a Web app that does a thing. Which service is do I use? You open up the service listing and you just get this sort of sinking sensation. I get that. I can't imagine what someone new to the space is getting to >> you, and this is where things like amplify fargate aws patch. You don't need to select an instance where you just tell us for your requirements are on Batch makes that collection for you the core building. What's important because you can't really figure out what to do. But then you see us too much more about the attack to help people get there. It's an ongoing thing that will keep trying to tackle, but you see a lot more of that. >> It's controversial. One of my favorite things about Lambda, for example, is there's one knob ram, and as you turn that up, other performance characteristics increase and people complain about it. But I love the simplicity because I don't have to sit and think and make all these different decisions. It's one access, >> but if you want more knob, you can you fuck it. So I think that that's the beauty ofit that you do have that choice. >> Yeah, one lines there, and I really liked it. Borders keynote. Is he said way? Really? You know my words, commoditized. I t We all have access to all of the tools now, you know that was you know what big date originally file. It also was used to have to be a nation state 4100 to be able to do some of these things. So, you know, what do you hear from customers? How do they make sure you know, they're staying competitive and ahead on their four in that relationship between the business and I T. What do you hear from your customers these days? >> In terms of that? Well, I think, um, for you know, for customers like I think of Emperor age is a, uh, a pretty good example off that in terms of customers asking us for ability to, you know, integrate their SAS providers and a great a lot of different things and not have thio you No, no, no. >> I have >> to do a lot of undifferentiated heavy lifting and things like that. And customers are increasingly moving towards, like avenger oven architectures. And they asked us, Hey, we really like cloudwatch events and how you do things with a iittie automation and then bringing SAS providers and on way wantto you know, we don't want to build a polling infrastructure and orderto access athe eyes and do all all the heavy lifting. What we did was we built out way took cloudwatch events and added new features for SAS applications and build that into a separate service for people to use. That's like, you know, a lot of the relationships we have our customers listening to what they need and giving them what they want. >> I think that that's a very valuable thing. We used to say, You know, five years ago you would talk about, you know, let's get rid of indifferent, heavy lifting Well, now it's like, No, no, let's enable you know some thing that you would have thought was heavy lifting and we're daunted to be able to do it. But now hopefully it's easier because a lot of this stuff, you know, he said, This is still a little bit daunting, and you know, you've got a lot of ecosystem and service providers, and service is help us. You take care of, you know, because it's the paradox of choice. With all the options that you >> have on. I think that's the beauty of what I'm in a customer that smart. They managed to find interesting ways to keep challenging us and keep us busy. But I also think that really, really many of them the ones who've been able to be successful. I figured out what it needs to be. Take all the tools to give them which other ones where they want to completely hand it over to AWS and give us the responsibility. And then which ones today really feeling, get they care about and the ones who can find their balance of the ones that we see moving faster. I think that's what we're trying to >> write that one thing that does absolutely permeates virtually every service team I've worked with that AWS. I mean, I've had this experience with you where I talk about how my use case isn't a terrific fit for your product, and your response is always well, what is your use case? It's not. Is starting off on the baseline assumption that my use cases ridiculous, which, let's face it, it probably is. But being able to address a customer need to understand that even if it doesn't dictate, road map is incredibly valuable, and I don't find there are too many players in any space, let alone this one that are willing to have the patience to listen to. Frankly, some loud person wearing a suit. >> Way try. I mean, I think you heard me say this so much like a big junk. 85 90% of a road map. Customer request. I would say that even though remember remaining 10% maybe not think that they're directly asked for but think that you observed their running to or that we run into working with, you know, the one of the customers go ahead of the pack. Okay. They have this problem, Baker. How do you generalize that? And we try and understand what it means. One of the reasons to be made the container road map public was This space is moving so quickly. It's almost impossible for us to talk to enough customers to figure that out. So, like, okay, that gives us an avenue for them to come to us and just tell us and get have >> issues. Yeah, s o right. Final question for both of you directions. Looking forward, you know, the road map we love when there is publicly facing material, not under the NBA's that we normally have to be able to hear. So what are you hearing from your customers? What direction are they pulling you towards and that we should expect tow watch aws kind of a cz we head towards reinvent later this year. Yeah, >> like customers are asking us for different things for developer experience, especially event driven architectures. I think there's gonna be a lot of interesting things happening in the land of space and that entire space >> on to add to that. I think your point earlier helping the simplified choices is going to be a big part of it. Meeting them where they are in their ideas with the cooling is a big part of what you'll see us do. So you know, I think you saw examples today. We'll keep building on top of >> All right. Well, send our congratulations to the two pizza teams that worked on all of the projects that were announced today. Look forward to seeing you. You know, down the road in tracking down. Thanks so much. And welcome to be in Cuba one night having us Deepak, you know, from AWS. He's Cory Quinn on student back with lots more coverage from 80 West Summit here in New York City. Thanks for watching

our Studios in the heart of Silicon Valley Palo Alto California this is a cute conversation hi welcome to the cube studio for another cube conversation where we go in-depth with thought leaders creating new business outcomes with technology I'm your host Peter Burris one of the biggest challenges that enterprises face as they try to get more value out of their data is how to establish as the practices how to establish the processes and the tooling necessary to both discover data liberate data and communicate data and its value to the organization to have that conversation we've got a great guest Aaron kal who's a co-founder and chief data officer revelation Aaron welcome to the conversation Peter thank you for having me so give us a quick update what's going on with relation things relations are very very exciting so you know from the very beginning we had one main goal to create technology that empowers people to be more curious make better choices to help them find relevant data understand the trust that uses it and we use it that the organization and we're just very happy to keep getting more and more customers and make a bother and broader impact through them now showing how unbelievably attentive I am I noticed that I that you are now chief data officer so your titles changed what does that entail what's going on yeah it's a pretty recent change in a moment were very excited about I think from the very beginning you know we've been preaching you know data-driven organization but we haven't been able to practice what we've preached as much since we've been comparatively so much smaller than our customers what's exciting now is that we've collected enough data and a wide enough network of customers there's an opportunity to really be more data-driven internally and also to kind of have all of these chief data officer x' and other data people they donors like me in our network to synthesize the learnings across all of them about how to build a data culture and kind of take that in and share it back out so we can all go through this journey together so I'm gonna tell you I have been a chief data officer skeptic but I'll explain why but if I could just summarize what you just said there's gonna be an operational part of your job generally but also an advocacy part of your job externally to help catalyze some of your conversations but let me tell you why I've been something of a skeptic and you tell me how things are gonna change you know what what vector were on so to speak see do as a job often was something that people went for because of digital change or a new media change and new types of marketing it's been a job that's been all over the map it's had different definitions different roles different sets of responsibilities when I think of any chief I say you give the chief title to someone who's going to generate you know superior returns on the assets entrusted to them so what that means to me is that the chief data officer should be someone who's going to create competitive or superior returns on the data assets that have been entrusted to them is that kind of how you see it - that's exactly right and this is a term in a title that we're borrowing from our customers who've been very very successful with it and and the goal is exactly that first of all to protect the data and ensure that it's being used appropriately and is well governed that's the defense but then going on offense and ensuring that all that data is actually driving business value and business impact that's the fundamental role of the position the only thing I would I would maybe amend what you said is as chief my management style is really it's just about empowering everybody in the organization within the division and across the company 2 billion drivers impact well it's a leadership job exactly yeah so chiefest you know you're supposed to you're supposed to use the resources at your disposal to generate returns out of those resources and it's obviously it's a leadership job but let's let's walk through that a little bit not so much focusing on how elations CDO is going to operate but let's talk about your customers because one of the observations I'd make is that elation now has a large enough footprint and presence in the industry where you now have significant numbers of customers and I'm sure you're seeing the variety of insights and practices that customers are using to get value out of data so I got to believe that partly this is discovering those new practices those new procedures turning that into a pedagogy something that folks can actually use to improve the way they do things and then helping alation build or participate in the tool change necessary to actually establish those disciplines how far off am i you are spot on so so as you said we have over a hundred production customers now well over that and they all are different in different ways depending on on their geography and their vertical but there are many commonalities we see and our goal is to basically learn from all of them and synthesize those learnings and then push them back out to our network and also apply them internally and sometimes applying that means making changes to our software and sometimes it means just sharing best practices and thought leadership within our network and and beyond so to give a very particular example you know one thing that we'd thought about a little bit but we really learned from our customers was the power that kind of competition and and and and kind of game theory can can play in helping people be successful in their data initiative so gamification gamification exactly yes so we saw for example some of our customers there what they called data duals or metadata duals where a different departments would compete to document their data more thoroughly for accurate outcomes and and they would get cakes they had you know metadata on them it's gotta fund on time we'd seen the word metadata printed on a cake probably in the history of baking an email a different customer in a different region different vertical came out with a doc you Jam which is taking the idea of a hackathon and that's a little bit less competitive and what more collaborative people kind of shoulder-to-shoulder doing data documentation it's a very similar thing of using kind of human psychology to better drive forward data projects we saw in two different places and we thought okay how can we have strapped out a principle from this and we're looking both at integrating some of these principles directly into our product and also sharing other ways the different customers could benefit from the basic concept all right so where are we you've got 100-plus hundred-plus customers now you're an acknowledged leader in the in the catalog world we generally believe the catalogs are going to be an important feature of virtually every successful data-driven digital business because it's going to be one of the places where you actually or data and other assets derived from that data models and whatnot so where are we as as this new CDO where are we in the adoption of what you today would regard as the best practices how is that happening in the industry we have a skills gap are we starting to see that be closed a bit as as more companies start to gain the experience they need to be successful in this yeah you know it's funny there's sort of a learning curve with any new technology or any principal and we see customers and prospects all along that curve and we start kind of mapping out the shape just to give a sense of different extremes you know a few years ago what everybody was talking about people would say I'm a data person and there's people in my company who just don't get it who see the data and instead of being appropriately skeptical and saying I'm not sure how this was sourced they'll just say add the other shmurda here's how I used to do it at my old job and we're just gonna do it that way because it's how we've always done it and you know there was there was that sort of a defensive Nisour resistance to data now we're seeing some customers who have jumped way past that I was talking to a data scientist at one of our customers who said basically they have a recommendation engine in their enterprise and people who years ago might have been completely ignoring it are now just blindly doing whatever it said and and she was saying his own set of implications it does and she said look you know as a data scientist I know how the sausage is made for this engine I wouldn't want to eat that sausage it worries me the people are just putting it on their mouths so to speak and this elaborate metaphor and so I think I think you know the pendulum can swing back and forth what we're trying to work on with our customers is how do you teach individuals to engage in that data culture to be skeptical in the right ways not defensive but to ask where did this come from how is it computed you know the questions that can actually help you interpret it correctly and put it to use and I'd go to the other extreme of you know basically a deferring to the algorithm entirely and taking out all human judgment well and I think that's the important thing is that all these any systems accommodation machines doing things and human beings doing things will take out those animal driven systems for many years ago machines doing things and people doing things and you when you use machines to do things the tech industry has been really good at diffusing knowledge very very quickly so it's over time it's difficult to have your machinery be the source of differentiation so over time humans will consistently be the source of differentiation in your business and how they render judgment and what they determine the priorities and the commitments that they make and sustaining keeping those commitments so catalog to me seems to be an especially important feature of any digital transformation or data-driven process going forward because it touches people and because people use it and it will will also touch other systems and other elements but people remain essential to catalog design and the notion of catalog experience are you seeing that as well and is that helping you to stay close to these CDOs and you know really driving the people oriented process or knowledge about people or any processes you know absolutely throughout our time adulation over the last you know seven years we've always seen people as extremely central and I think one of our key differentiators philosophically was where a lot of data management was sort of thinking about what's good for the computer oh we can save a couple bits by using some lookup code that doesn't mean that's you know comprehensive all we said well what is the human consumer of data what do they need and a lot of our technology has actually been again bringing the human back into the fold of what's been to kind of computer and machine dominated and then the other thing you mentioned it's really critical is we're in an age where automation is very exciting there are a lot of wins there one thing that I hear from C do after C do as I talk to is a three-phased process for bringing data into the organization phase one is is descriptive analytics what happened last quarter then there's predictive analytics what's gonna happen next quarter and the final goal is prescriptive analytics for your computer what holds you what to do well yeah and and where the computer can act you know before any humans even looked at it or been in the loop and I think it's an interesting aspiration especially for certain things that are really really urgent but these are all garbage in garbage out processes and the good news is that if you're looking at a place where the humans in the loop they can say you know what that doesn't look right in that graph and maybe it's a problem with the ETL job or with thesaurus data and they can set them for something Bad's happened so I think as you progress down this evolution there are great rewards but also greater risks and our hope is that with a catalog you can make sure that whatever process you're feeding instead of garbage in garbage out it's the best data that's up-to-date that's trustworthy its contextualized for the business process ok one last question you've you're now in a new role operational external what's the first two things that you want to accomplish in this new role especially on the as you as it pertains to working with your customers what are you really focused on right now yeah so one of our core values at halation is that we listen as though we could be wrong because we know that that's part where data company is you know how do we learn from from numeric and other kinds of signals that come in to always be growing and improving and so step one unambiguously is to listen as much as I can to the incredibly smart innovative thoughtful customers that we have and try to synthesize the best learnings across all of them I think the next step is to then is to then do that synthesis and say oh what do we see this happening in retail that could pertain to finance or vice versa and figure out kind of what is that that curve and how can we kind of either push everybody up the steep parts of the curve so we can all be more data-driven and more curious and I'm more rational together or even have you know the software kind of lower that curve and right before your two great points so it's faster up or use the tool to flatten a curve exactly it's very wise man interview well Aaron this has been a great conversation once again I want to thank you for joining us on another cube conversation my name is Peter Burris see you next time thank you Peter you [Music]

>> Hello everyone. Welcome to this Cube conversation here in Palo Alto. On John Furrier, co host of the Cube. I'm here. Aaron Kalb is the co founder and VP of design and Alation. Great to see them on some fresh funding news. Aaron, Thanks for coming. And spend the time. Good to see you again. >> Good to see you, John. Thanks for having me >> So big news. You guys got a very big round of financing because you go to the next level. A startup. Certainly coming out that start up phase and growth phase super exciting news. You guys doing some very innovative things around, date around community around people and really kind of cracking the code on this humanization democratization of data, but actually helping businesses. I want to talk about it with you. First. Give us the update on the financing, the amount what it means to the company. A lot of cash. >> Yeah. So we're very excited to have raised a fifty million dollar round. Sapphire led the round, and we also had, you know, re ups from all of our existing investors. And, you know, as as a co founder, he always had big dreams for growth. And it's just validating tohave. Ah, a community of investors who can see the future, too, as well as our great community of over one hundred customers now who want to build this data democratized future with us. >> We've been following you guys since the founding obviously watching you guys great use of capital. Fifty million's a lot of capital, so obviously validation check. Good, good job. But now you go to a whole other level growth. What's the capital gonna be deployed for? What's going on with company where you guys I and in terms of innovation, what's the key focus? >> It's a great question. So you know, obviously we have revenue from our customers. But getting this extra infusion from VC lets us just supercharge our development. It's growth. It's going to more customers, both domestically and abroad, goingto a broader user base. And we're Enterprise-wide Adoption within those customers, as well as innovation in the core product, new technology, great design and futures. that are really going to change the organization's access and use data to make better decisions? >> What was the key Learnings As you guys went into this round of funding outside the validation to get through due diligence, all that good stuff. But you guys have made some successful milestones. What was the key? Notable accomplishments that Alation hit to kind of hit this trigger point here for the fifty million? >> Yeah, I'm glad you asked about that. I think that the key thing that's changed it's enabled this. This next phase is that the data catalog market has really come into its own right. In the beginning, in the early days, we were knocking on doors, trying to say, You know, we don't even know it was going to be called data catalog in our first few months. And even though we had the technology, we said, Hey, we got this thing and we know it's useful. Please buy it. Please want it. And the question was, you know, what's the data catalog by what I ever even look at that? And it's just turned a corner. Now, you know, Thanks. In part of things like Gartner telling companies you know, in the next year by twenty twenty, if you have a data catalog, you're goingto see twice the ROI from your existing data investments than if you don't your stories like that are making companies say? Of course, you want to data catalog. It just turned out a dime. Now they're asking, Which data catalog should we get? Why is yours the best in this change of the market maturing? I think it's the biggest change we've seen >> with one thing that we've observed. I want to get your reaction to This is that I'll stay with cloud computing economics, a phenomenally C scale data data science working the cloud. We see great success there. Now there's multiple clouds, multi clouds, a big trend, but also the validation that it's not just all cloud anymore. The on premises activity steel is relevant, although it might have a cloud. Operations really kind of changes the role of data. You mentioned the data catalogue kind of being kind of having a common mainstream visibility from the analysts like Gardner and others on Wiki Bond as well. It makes data the center of the innovation. Now you have data challenges around. Okay, where's the data deployed? Where my using the data? Because data scientists want ease of data, they want quality data. They want to make sure their their algorithm, whether it's machine learning component or software actually running a good data. So data effectiveness is now part of the operations of most businesses. What's your reaction to that? Which your thoughts. Is that how you see it? Is there something different there? What's going on with the whole date at the center? >> Absolutely hit on two key themes for us. One of that idea of the center and the other is your point about data quality and data trust. So, so centrality, we think, is really essential. You know, we're seeing cataloging technology crop up more and more. A lot of people were coming out with catalogs or catalog kind of add ons to their products. But what our customers really tell us is they want the data catalog to be the hub, that one stop shop where they go to to access any data, wherever it lives, whether it's in the cloud or on Prem, whether it's in a relational database or a file system, so is one of Alations key. Differentiators early on was being that central index, much like Google is out of the front page to the Internet, even though it's linking to ad pages all over the place. And the other thing in terms of that data quality and data trustworthiness has been a differentiator, and this was something that was part of our technology when we launched that we didn't put the label out till later. Is this idea of Behavior IO, that's kind of looking at previous human behavior to influence future human behavior to be better. And there's another place we really took some inspiration from Google and Terry Winograd at Stanford before that, you know, he observed. You know, if you remember back before Google search sucked, frankly, right, the results on top are not the most development were not the most trustworthy. And the reason was those algorithms were based on saying, how often does your key word appear in that website? Built, in other words, and so you'd get results on top. That might just not be very good. Or even that were created by spammers who put in a lot of words to get SEO and and, you know, that isn't the best result for you on what Google did was turned that around with page rank and say, Let's use the signals that other people are getting behind about the pages they find valuable to get the best result on top. And Alation is the exact same thing our patented proprietary behavior technology lets us say Who's using this data? How were they using it? Is it reputable? And that enables us to get the right data and transfer the data in front of decision makers. >> And you call that Behavioral IO >> Behavior IO, that's right. >> I mean, certainly remember Google algorithmic search was pooh poohed. It first had to be a portal. Everyone kind of my age. You can't remember those those days and the results were key word stuff by spammer's. But algorithmic search accelerated the quality. So I got to ask you the behavioral Io to kind of impact a little bit. Go a little deeper. What does that mean for customers? Because now I'll see as people start thinking, OK, I need to catalogue my data because now I need to have replication, all kinds of least technical things that are going on around integrity of the data. But why Behavioral Aya? What's the angle on that? What's the impact of the customer? Why is this important? Absolutely so. >> Might have to work through an example, you know we joke about. You might be looking around in your SharePoint drive and find an Excel file called Q three Numbers final. Underscore final. Okay, that seems that'S inject the final numbers, and then you see next to it when it says underscore final underscore, final underscore finalist. Okay, well, is that one final? And it turns out what Data says about itself is less reliable than what other people say about the data. Same thing with Google that if everyone's linking with Wikipedia Page, that's a more reliable page than one that just has, you know, paid for a higher placement, Right? So what a means an organization is with Alation will tell you. You know, this is the data table that was refreshed yesterday and that the CFO and everybody in this department is using every day. That's a really strong signal. That's trustworthy data, as opposed to something that was only used once a year ago. >> So relevance is key there. >> Absolutely. It's relevant. And trustworthiness. We find both all right, indicated more strongly by who's using it and how than by the data itself. >> Are you seeing adoption with data scientist and people who were wrangling date or data analysts that if the date is not high quality, they abandoned. The usage is they're getting kind of stats around that are because that we're hearing a lot of Hey, you know, that I'm not going to really work on the data. But I'm not going to do all the heavy lifting on the front end the data qualities, not there. >> Absolutely. We see a really cool upward spiral. So in Alation, we have a mix of manual, human curated metadata, you know, data stewards and that a curator saying, this is endorsed data. It's a certified data. This is applicable for this context. But we also do this automatic behavior. Io. We parse the query logs. These logs were, you know, put there for audit on debugging purposes. But we were mining that for behavioral insight, and we'll show them side by side on what we see is overtime on day one. There's no manual curation. But as that curation gets added in, we see a strong correlation between the best highest quality data and the most used data. And we also see an upward spiral where, if on day one. People are using data that isn't trustworthy that stale or miscalculated as soon as Ah, an Alation steward slaps a deprecation or a warning on the data asset because of technology like trust check talking about last time I was here, that technology, that's the O part of behavior IO We then stop the future behavior from being on bad data, and we see an upward spiral where suddenly the bad sata is no longer being used and everyone's guided put the pound. >> One thing I'm really impressed with you guys on is you have a great management team and overall team with mixed disciplines. Okay, I think last night about your role, Stanford and the human side of the world. But you have to search analogy, which is interesting because you have search folks. You got hardcore data data geeks all working together. And if you think about Discovery and navigation, which is the Google parent, I need to find a Web page and go, Go, go to it. You guys were in that same business of helping people discover data and act on it or take action. Same kind of paradigm, so explain some customer impact anecdotes. People who bought Alation, what your service and offering and what happened after and what was it like before? We talk about some of that? And because I think you're onto something pretty big here with this discovery. Actionable data perspective. >> Yeah, well, one of our values, it Alation, is that we measure our success through customer impact, you know, not do financing or other other milestones that we are excited about them. So I I would love to talk about our customers. One example of a business impact is an example that our champion at Safeway Albertsons describes where, after safe, it was acquired by Albertson's. They've been sort of pioneers of sort of digital, ah, loyalty and engagement. And there was a move to kind of stop that in its tracks and switch should just mailing people big books of coupons that of customizing, you know, deals for you based on your buying behavior. And they talked about getting a thirty x  ROI on the dollars they've spent on Alation by basically proving the value of their program and kind of maximizing their relationship with their customers. But the stories they're even more exciting to me, then just business impacts in dollars and cents when we can leave a positive impact on people's lives with data. There's a few examples of that Munich reinsurance, the biggest being sure and also a primary ensure in Europe, had some coverage and Forbes about the way that they use Alation, other data tools to be able to help people get back on their feet more quickly after, ah, earthquakes and other natural disasters. And similarly, there's a piece in The Wall Street Journal about how Pfizer is able to create diagnostics and treatments for rare diseases where it wouldn't have been a good ROI even invest in those if they didn't get that increased efficient CNN analytics from Alation on the other data. >> So it's not just one little vertical. It's kind of mean data is horizontally. Scaleable is not like one. Industry is going to leverage Alation, >> Absolutely so you know, I mentioned just now. Insurance and health care and retail were also in tech were in basically every vertical you can imagine and even multiple sectors. You know, I've been focusing on industry, but there's another case that you can read about at the city of San Diego were there. They're doing an open data initiative, enabling people to figure out everything from where parking is easiest, the hardest to anything else. >> The behavioral Io. And it's all about context and behavior, role of data and all this. It's kind of fundamental to businesses. >> That's right. It's all about taking everything about how people using data today and driving people to be even more data driven, more accurate, better able to satisfy their curiosity and be more rational in >> the future. So if I'm a from a potential customer and I heard a rAlation, get the buzz out there, why would I need you? What air? Some signals that would indicate that I should call Alation. What's some of that Corvette? What's the pitch? >> Yeah, it's a great question. No, I sometimes joke with the team that you know every five minutes another enterprise reaches that point where they can't do it the old way anymore. And the needle ations. And the reason for that is that data is growing exponentially and people can only grow at most, you know, linearly. So I compare it a bit again to the days of of Yahoo When the Internet was small, you make a table of contents for it. But as there came to be trillions of red pages, you needed an automatic index with pay drink to make sense of it. So I would say, once you find that your analytics team has spread out and they're spending, you know eighty percent of their time calling up other people to find where development data is, you're asked to Your point is this data high quality show even spend my time on it? You know that's probably not money is well spent with these highly paid people spending other times scrounging If you switch from scrounging to finding understanding and trusting their data for quick and accurate analysis, give us >> a call. So basically the pitches, if you want to be like Yahoo, do it the old way. We know what happened. Yeah, you want to be like Google, two algorithmic and have data >> God rAlation, and you'll be around for a while very well. After that, maybe the one see that that's my words. >> And and that's part of turning that corner. I think in the beginning we were trying to tell people this could be a nice toe have. And now customers are coming to us realizing it's a must have to stay a relevant, you know, And if you've made all these investments in data infrastructure and data people, but you can't connect the dots is you said, between the human side and the tech side that money's all wasted and you're going to not be able to compete against your competitors and impact of customers what you want. >> Well, Eric, congratulations. Certainly is the co founder. It's great success. And how hard is that you start ups? You guys worked hard and again. Why following you guys? Been interesting to see that growth and this innovation involved in creative, A lot of energy. You guys do a good job. So final question, talk about the secret sauce of Alation. What's the key innovation formula? And now that you got the funding where you're going to double down on, where's the innovation going to come next? So the innovation formula and where the innovation, the future, >> absolutely innovation has been critical for us to get here on our customers didn't just buy the exciting features with behavioral and trust. Check that we had but also are buying into the idea that we're going to continue to be the leaders and to innovate. Andi, we're going to do that. So I think the secret sauce which we've had in the past, we're going to continue to innovate in this vein, is to be really conscious of water computers great at and what humans uniquely good at what you humans like doing and trying to have the human and computers work together to really help the human achieve their goals. Right? So, Doctor, the Google example. You know, there's a bunch of systems for collaboratively ranking things, but it takes work to, you know, write a review on the upper Amazon. Google had the insight that we could leverage people are already doing and make it about it. Out of that, we're going to continue to do that. >> The other kind of innovation you'll see is bringing Alation to a wider and wider audience, with less and less technical skill needed. So I came from Syria Apple, and the idea is you have to learn a programming language to Queria database. You could just speak in English. That helps you ask answer questions like What's the weather today? Imagine taking that same kind of experience of seamless integration to the more important questions enterprises are asking. >> We'll have to tap your expertise is we want to have an app called the Cube Syria, which is a cube. What's the innovation in Silicon Valley and have it just spit out a video on the kidding? Final question just to double down on that piece, because I think the human interactions a big part of what you're saying I've always loved that about with your vision is. But this points to a major problems. Seeing whether it's, you know, media, the news cycle These days, people are challenging the efficacy of finding the research and the real deep research on the media. So I was seeing scale on data scale is a huge challenge. You mentioned the growth of data. Computers can scale things, but the knowledge and the curation kind of dynamic of packaging it, finding it, acting on it. It's kind of where you guys are hitting. Talk about that tie name, my getting that right and set is that important? Because, you know, certainly scale is table stakes these days. >> That is super insightful John, because I think human cognition and human thought excuse me, is the bottleneck four being data driven right we have on the Internet trillions of Web pages, you know, more than the Library of Alexandria a hundred times over, and we have in databases millions of columns and trillions of rose. But for that to actually impact the business and impact the world in a positive way, it's got to go through a person who could understand it. And so, in the same way that Google became the mechanism by which the Internet becomes accessible, we think that Alation for organizations is becoming the way that data can become actionable. And the other thing I would say is, you know, in this age of alternative facts and mistrust of data, you know, we've sort of realizing the just having more information out there doesn't actually make people wiser and better able to reason. It can actually be a lot of noise that muddies the signal and confuses people. So we think Alation by also using human computer interaction to help separate the signal from the noise and the quality from the garbage can help stop the garbage in garbage out and make people more rational and more curious and have more trust than what there. Hearing understanding >> build that Paige rang kind of metaphor is interesting because the human gestures, whether it's work or engaging on the data, is a signal tube, not just algorithmic meta data extraction. >> Absolutely anything you do with data and any tool, even outside of Alation. Alation will capture that and use it to guide future behavior for you and your appears to be better and smarter. >> Fifty million dollars. Where's this all going to lead to wins the next innovation. What do you guys see? The future for rAlation? >> Well, you know, I, uh I was just thinking before the show I used to be an apple kind of in the golden Age when Apple was really innovative. And there was the joke where they released something new and say, Redman, start your photocopier. So in this interview, I'm going to be a little close to the chest about the specifics, but we're releasing. But I will tell you we have a room that we're really excited about to go to a broader and broader audience that impactor customers more fully >> well you feel free to say one more thing? >> Yeah. I think the secret to the future is Aaron. Thanks for coming on. >> Really preachy. Congratulations on the funding. He has got a very innovative formula. Good luck. And we'll be following you guys. Thanks, but come on, keep commerce. Thanks so much. Eric Kalb, co founder and VP of designing Alation. Interesting formula. Great. Successful. Former great innovation. Alation. Check him out. I'm Jennifer here in Palo Alto for cube conversation. Thanks for watching.

(stirring music) >> Hi, I'm Peter Burris, and welcome to another CUBE Conversation from theCUBE Studios in beautiful Palo Alto, California. Got a great conversation today. We're going to be talking about some of the new advances that are associated with big data analytics and improving the rate at which human beings, people who actually work with data, can get more out of their data, be more certain about their data, and improve the social system that actually is dependent upon data. To do that, we've got Aaron Kalb of Alation here with us. Aaron is the co-founder and is VP of design and strategic initiatives. Aaron, welcome back to theCUBE. >> Thanks so much for having me, Peter. >> So, then, let's start this off. The concern that a lot of folks have when they think about analytics, big data, and the promise of some of these new advanced technologies is they see how they could be generating significant business value, but they observe that it often falls short. It falls short for technological reasons, you know, setting up the infrastructure is very, very, difficult. But we've started solving that by moving a lot of these workloads to the cloud. They also are discovering that the toolchains can be very complex, but they're starting to solve that by working with companies with vision, like Alation, about how you can bring these things together more easily. There are some good things happening within the analytics space, but one of the biggest challenges is, even if you set up your pipelines and your analytics systems and applications right, you still encounter resistance inside the business, because human beings don't necessarily have a natural affinity for data. Data is not something that's easy to consume, it's not something easy to recognize. People just haven't been trained in it. We need more that makes it easy to identify data quality, data issues, et cetera. Tell us a little bit about what Alation's doing to solve that human side, the adoption side of the challenge. >> That's a great point and a great question, Peter. Fundamentally, what we see is it used to be a problem of quantity. There wasn't enough ability to generate data assets, and to distribute them, and to get to them. Now, there's just an overwhelming amount of places to gather data. The problem becomes finding development data for your need, understanding and putting it into context, and most fundamentally, trusting that it's actually telling you a true story about the world. You know, what we find now is, as there's been more self-service analytics, there's more and more dashboards and queries and content being generated, and often an executive will look at two different answers to the same question that are trending in totally different directions. They'll say, "I can't trust any of this. "On paper, I want to be data-driven, "but in actuality, I'm just going to go back to my gut, "'cause the data is not always trustworthy, "and it's hard to tell what's trustworthy and what's not." >> This is, even after they've found the data and enough people have been working on it to say, to put it in context to say, "Yes, this data is being used in marketing," or, "This data has been used in operations production." there's another layer of branding or whatnot that we can put on data that says, "This data is appropriate for use in this way." Is that what we're talking about here? >> Absolutely right. To help with finding and understanding data, you can group it and make it browsable by topic. You can enable keyword search over it in that natural language. That's stuff that Alation has done in the past. What we're excited to unveil now is this idea of trust check, which is all about saying, wherever you're at in that data value chain of taking raw data and schematizing it and eventually producing pretty dashboards and visualizations, that at every step, we can ensure that only the most trustworthy data sets are being used, because any problem upstream flows downstream. >> So, trust check. >> Trust check. >> Trust check, it's something that comes out of Alation. Is it also being used with other visualization tools or other sources or other applications? >> That's a great question. It's all of the above. Trust check starts with saying, if I'm an analyst who wants to create a dashboard or a visualization, I'm going to have to write some SQL query to do that. What we've done in that context with Alation Compose, is our home-grown SQL tool, is provided a tool, and trust check kind of gets its name from spell check. It used to be there was a dictionary, and you could look it up by hand, and you could look it up online, but that's a lot of work for every single word to check it. And then, you know, Microsoft, I think, was the first innovative saying, "Oh, let's put a little red squiggle that you can't miss "right in your workflow as you're writing, "so you don't have to go to it, it comes to you." We do the exact same thing. I'm about to query a table that is deprecated or has a data quality issue. I immediately see bright red on my screen, can't miss it, and I can fix my behavior. That's as I'm creating a data asset. We also, through our partnerships with Salesforce and with Tableau, each of whom have very popular visualization tools, to, say. if people are consuming a dashboard, not a SQL query, but looking at a Tableau dashboard or a visualization in Salesforce Einstein Analytics, what would it mean to badge right there and then, put a stamp of approval on the most trustworthy sources and a warning or caveat on things that might have an upstream data quality problem? >> So, when you say warning or caveat, you're saying literally that there are exceptions or there are other concerns associated with the data, and reviewing that as part of the analytic process. >> That's exactly right. Much like, again, spell check underlines, or looking at, if you think about if I'm driving in my car with Waze, and it says, "Oh, traffic up ahead, view route this way." What does it mean to get in the user interface where people live, whether they're a business user in Salesforce or Tableau, or a data analyst in a query tool, right there in their flow having onscreen indications of everything happening below the tip of the iceberg that affects their work and the trustworthiness of the data sets they're using. >> So that's what it is. I'll tell you a quick story about spell check. >> Please. >> Many years ago, I'm old enough that I was one of the first users of some of these tools. When you typed in IBM, Microsoft Word would often change it to DUM, which was kind of interesting, given the things that were going on between them. But it leads you to ask questions. How does this work? I mean, how does spell check work? Well, how does trust check work, because that's going to have an enormous implication. People have to trust how trust check works. Tell us a little bit about how trust check works. >> Absolutely. How do you trust trust check? The little red or yellow or bright, salient indicators we've designed are just to get your attention. Then, as a user, you can click into those indicators and see why is this appearing. The biggest reason that an indicator will appear in a trust check context is that a person, a data curator or data steward, has put a warning or a deprecation on the data set. It's not, you know, oh, IBM doesn't like Microsoft, or vice versa. You know, you can see the sourcing. It isn't just, oh, because Merriam-Webster says so. It emerges from the logic of your own organization. But now Alation has this entire catalog backing trust check where it gives a bunch of signals that can help those curators and stewards to decide what indicators to put on what objects. For example, we might observe, this table used to be refreshed frequently. It hasn't in a while. Does that mean it's ripe for getting a bit of a warning on it? Or, people aren't really using this data set. Is there a reason for that? Or, something upstream was just flagged having a data quality issue. That data quality issue might flow downstream like pollution in a creek, and that can be an indication of another reason why you might want to label data as not trustworthy. >> In Alation context with Salesforce and Tableau partners, and perhaps some others, this trust check ends up being a social moniker for what constitutes good data that is branded as a consequence of both technological as well as social activities around that data captured by Alation. I got that right? >> That's exactly right. We're taking technical signals and social signals, because what happens in our customers today before we launched trust check, what they would do is, if you had the time, you would phone a friend. You'd say, "Hey, you seem to be data-savvy. "Does this number look weird to you? "Do you know what's going on? "Is something wrong with the table that it's sourced from?" The problem is, that person's on vacation, and you're out of luck. This is saying, let's push everything we know across that entire chain, from the rawest data to the most polished asset and have all that information pushed up to where you live in the moment you're making a decision, should I trust this data, how should I use it? >> In the whole, going back to this whole world of big data and analytics, we're moving more of the workloads to the cloud to get rid of the infrastructure problems. We're utilizing more integrated toolchains to get rid of the complexity associated with a lot of the analytic pipelines. How does trust check then applied, go back to this notion of human beings not being willing to accept somebody else's data. Give us that use case of how someone's going to sit down in a boardroom or at a strategic meeting or whatever else it is, see trust check, and go, "I get it." >> Absolutely, that's a fantastic question. There's two reasons why, even though all organizations, or 80% according to Gartner, claim they're committed to being data-driven. You still have these moments, people say, "Yeah, I see the numbers, "but I'm going to ignore them, or discount them, "or be very skeptical of them." One issue is just how much of the data that gets to you in the boardroom or the exec team meeting is wrong. We had an incredibly successful data-driven customer who did an internal audit and found that 1/3 of the numbers that appeared in the PowerPoint presentations on which major business decisions were being made, a full 1/3 of them were off by an extraordinary amount, an amount so big that it would, the decision would've cut the other way had the number been accurate. The sheer volume of bad data coming in to undermine trust. The second is, even if only 5% of the data were untrustworthy, if you don't know which is which, the 95% that's trustworthy and the 5% that's not, you still might not be able to use it with confidence. We believe that having trust check be at every stage in this data value chain will solve, actually, both problems by having that spell-check-like experience in the query tool, which is where most analytics projects start. We can reduce the amount of garbage going into the meeting rooms where business choices are being made. And by putting that badge saying "This is certified," or, "Take this with a grain of salt," or, "No, this is totally wrong," that putting that badge on the visualizations that business leaders are looking at in Salesforce and Tableau, and over time, in ideally every tool that anybody would use in an enterprise, we can also help distinguish the wheat from the chaff in that context as well. We think we're attacking both parts of this problem, and that will really drive a data-driven culture truly being adoptable in an organization. >> I want to tie a couple things that you said here. You mentioned the word design a couple times. You're the VP of design at Alation. It also sounds like when you're talking about design, you're not just talking about design of the interface or the software. You're talking about design of how people are going to use the software. What is the extent to which design, what's the scope of design as you see it in this context of advanced analytics, and is trust check just a first step that you're taking? Tell us a little bit about that. >> Yeah, that's a great set of questions, Peter. Design for us means really looking at humans, and starting by listening and watching. You know, a lot of people in the cataloging space and the governance space, they list a lot of should statements. "People should adopt this process, "because otherwise, mistakes will be made." >> Because Gartner said 80% of you have! >> Right, exactly. We think the shoulds only get you so far. We want to really understand the human psychology. How do people actually behave when they're under pressure to move quickly in a rapidly changing environment, when they're afraid of being caught having made a mistake? There's all these pressures people are under. And so, it's not realistic to say, again, you could imagine saying, "Oh, every time before you go out the door, "go to MapQuest or some sort of traffic website "and look up the route and print it out, "so you make sure you plot correctly." No one has time for that, just like no one has time to look up every single word in their essay or their memo or their email and look it up in the dictionary to see if it's right. But when you have an intervention that comes into somebody's flow and is impossible to miss, and is an angel on your shoulder keeping you from making a mistake, or, you know, in-car navigation that tells you in real time, "Here's how you should route." Those sort of things fit into somebody's lifestyle and actually move impact. Our idea is, let's meet people where they are. Acknowledge the challenges that humans face and make technology that really helps them and comes to them instead of scolding them and saying, "Oh, you should change your flow in this uncomfortable way "and come to us, "and that's the only way "you'll achieve the outcome you want." >> Invest the tool into the process and into the activity, as opposed to force people to alter the activity around the limitations or capabilities of the tool. >> Exactly right. And so, while design is optimizing the exact color and size and UI/UX both in our own tools and working with our partners to optimize that, it's starting at an even bigger level of saying, "How do we design the entire workflow "so humans can do what they do best "and the computer just gives them "what they need in real time?" >> And as something as important, and this kind of takes it full circle, something as important and potentially strategic as advanced analytics, having that holistic view is really going to determine success or failure in a lot of businesses. >> That is absolutely right, Peter, and you asked earlier, "Is this just the beginning?" That's absolutely true. Our goal is to say, whatever part of the analytics process you are in, that you get these realtime interventions to help you get the information that's relevant to you, understand what it means in the context you're in, and make sure that it's trustworthy and reliable so people can be truly data-driven. >> Well, there's a lot of invention going on, but what we're really seeking here is changes in social behavior that lead to consequential improvements in business. Aaron Kalb, VP of design and strategic initiatives at Alation, thanks very much for talking about this important advance in how we think about analytics. >> Thank you so much for having me, Peter. >> This is, again, Peter Burris. This has been a CUBE Conversation. Until next time. (stirring music)

>> Announcer: Live from Las Vegas, it's theCUBE. Covering AWS Reinvent 2017, presented by AWS, intel, and our ecosystem of partners. >> Welcome back to theCUBE's continuing coverage of AWS Reinvent 2017. This is day two for us. Incredible day one. We had great buzz on day two. Great announcements coming out from AWS today. I'm Lisa Martin with my cohost Keith Townsend, and we're excited to be joined by CUBE alumni, Aaron Kalb, the head of product and a founder of Alation. Welcome back to the show. >> Thanks so much for having me. I'm excited to be here. >> So speaking of excitement, you can hear the buzz behind us. Interesting about Alation, the first data catalog designed for human collaboration. What gap did Alation see in the market five years ago when you started? >> That's a great question, Lisa. So, yeah, we're the first data catalog, period, and we're excited to see a lot of other people kind of using that label, I believe it validates this as a space, and I think that everybody needs, and I think our approach, as you said, was to really to approach it from the human side, to say the data might be generated by machines or stored on machines, but it's not meant to ultimately be consumed by machines. Even if there's algorithms that's pulling it in, it's to ultimately serve human interests. So the goal was to design from the human back and really think, what does this data mean? Can I trust it? Is it gonna drive the processes correctly? >> So Aaron, I have seen that term quite a bit, and data catalog, for me, means one specific thing. Can you kind of wrap that up for us? >> What is a data catalog? >> That's a really great question, Keith, and I think what's interesting is we took a lot of inspiration in the early days actually from Amazon.com, right? So Amazon is an amazing modern product catalog. You can go in, type in English and see a variety of products that match that keyword. And for each one you can see whose bought it before, how many stars did they give it? Is it good? So it helps you find, understand, and trust, and get the right product for your need. We want to do that same thing for data. How do you found a trustworthy data asset, understand what it is, and put it to use? So that's exactly the goal. >> So, a simple problem is I've worked with a ton of researchers in the Big Pharma industry, data across the world basically. And a lot of data sets, repetitive. A team in Germany is working with one set of data, team in New Jersey working with another one, how does your solution help those researchers find the data that they're looking for? >> Exactly right. So the problem is many different data sets, many different things claiming to be true. Some of them are just plain wrong. Sometimes the answer might be one thing in Germany but something else elsewhere, and they're both valid. And so you've hit the nail on the head. The way people use data contains a lot of hints about the way you should use data. So just like Amazon, again, because we're here. And it'll say, oh, customers who bought what you're about to buy also bought this, and that can help you discover something useful. We try to expose we call behavior IO. Let the past behavior of the most knowledgeable people in the organization drive the future behavior. That's a big part of what we do. So one of the things I was reading about you guys on your website and some editorials is, a lot of data lakes fail. Why is that? How is Alation different? >> That's a great question. So I think what's interesting about a data lake is it's kind of like having a huge basement, right? And it can make you adopt a hoarder mentality, you say, oh it's so cheap to store everything, we'll just store it, and then when we need it we'll figure it out then. Well, the truth is, it's not always how it goes. Often you store so many things, it's cheap to store it, but when that actual human who has an actual analytical question they want to answer or an actual business process they want to improve, goes looking for the data, all they see are all these unlabeled boxes. Right? So I think the key is to think about how do you make information searchable, discoverable, understandable, trustworthy? And what's great is a lot of people are migrating from their on-premise data lakes to the Clouds, and obviously (mumbles) a big leader in where that's going. It gives you an opportunity to ask, just like when you move houses to say, let me look at what I've got, and can I adopt an approach? You know, what do I actually need? You might keep it all, but what's gonna be in the top shelf? What's gonna be in the basement? And how do you make everything accessible? >> So Aaron, can you talk a little bit about today's announcements? A lot of machine learning, analytics announcements from AWS. However, I don't know what I already have. So how can I make use of that data? Can you help talk about how Alation helps to leverage some of these new tools from AWS? >> Absolutely. So, we've had a bunch of customers on AWS Stack already, and increasingly so. Fundamentally our customers are people who do analysis. A lot of them are using S3, Redshift, the like. And people are hosting on the Cloud increasingly. And it's exactly the problem you described. It's I know I have it somewhere, but I can't get my head around what I already have. What region is it in? >> Aaron: Exactly. >> Is it in a region, is it in my data center, where is it? >> Exactly. so whether that data is in Redshift, in S3, or somewhere else. Maybe it's, you know, in a Postgres or SQL Server or Oracle Server. (mumbles) hosted one. Whatever it is, we crawl and index everything you have, just the way Google crawls and indexes everything out on the web, and we make it searchable, and we put information about who's used it and how good it is front and center, just the way you can say, oh this is a five-star clock on Amazon, I'm gonna go click buy it now. >> So one challenge with data lakes is security around that data. So data catalog, I get meta data around the data that I have, but some of that data is sensitive. How do you guys handle security around the data catalog itself? >> Absolutely. So we respect all the security and privacy settings that exist that are on the data itself, and we just sort of surface those in the catalog. Some of our customers say, look, we want to let people know what exists so they can ask for permission. Others say, even having awareness of this data is too much for us. And you mentioned, Pharma, that'll vary by industry. >> Where do you guys get involved in the customer conversation? You said many customers of yours are already using AWS for different things, but where does Alation come into the conversation? Are you brought in by AWS? Are you brought in by customers? Where are they on this journey towards leveraging the Cloud for the things that they need, agility, the speed, and the cost reduction? >> Absolutely. So our promise is we help you find, understand, and trust your data wherever it lives and whoever you are, democratizing it. So customers choose the right infrastructure for their needs, given cost, given performance. Obviously Amazon is increasingly a part of that. But that's a choice they make, and we resolve to handle that wherever it is. And as of customers, our customers are so smart, we learn so much from them. We're meeting a bunch of CIOs, both the prospects and also talking some current customers like Expedia today here at AWS lunch with our investor Costanoa and another at dinner tonight. And folks like Chegg and Invoice2go who've been longstanding AWS customers using S3, using Redshift, and actually in Chegg's case, they have a lot of homegrown tooling that they developed on the backend, but they said Alation is the best place to surface that and have it be the central portal for business users and analysts who might not be able to otherwise access things that are just available via (mumbles) >> So how are you, Alation, and AWS helping a customer like Chegg extract ROI quickly? >> Yeah, it's a great question, so, AWS is really great for cost containment. You have all this data and all this processing, but you have peaks and you have troughs, and how do you make sure you're not overpaying (mumbles) so it's great for helping with storage and computation. And Alation helps with the human side, how do you get that upside by saying you have this data, that could effect the way you stock your shelves, the way you price your products or who you hire, what markets you go into. And that requires that last step. If you have the data but it isn't in the right hands at the right time or it's interpreted incorrectly, it has no value. So the two of them together (mumbles) end-to-end solution. >> So Aaron, with GDPR coming up quick, the enforcement of that coming up May 2018, customers have to be concerned about having data they shouldn't have. Does Alation help identify some of that data? >> Absolutely. So data catalog is fundamentally an inventory of everything you have, plus information about how it has been and could be consumed. We very much focus on the upside, potential of using that to drive better business choices and better analysis. But we have customers actually saying, oh, we can use that same information about what we have, who's using it, what's in it, to instead make sure that it's used compliantly with a regulation like GDPR to make sure that you aren't holding onto health records longer than you should or PII. And it's absolutely a very big use case for many of our customers. >> So data is touched by a lot of people in an organization. AWS has done a great job of really developing a lot of synergy with the developer community for a long time now. But we're also seeing some trends suggesting they're going up the stack. They want to get more enterprises, enterprises are at the precipice, as Andy Jassey said, of this mass migration to the Cloud. You mentioned, all of your work with AWS and the CIO events that you're having here. Where are you guys in a conversation with customers? Are you more now having to get to that C-suite as now their business are absolutely predicated upon the best use of data to identify ways to monetize new revenue streams. How influential is that C-level in this conversation. >> It's a great question. So I think what is interesting is, all companies, we sort of commoditized a basic business school, consultant, best practice knowledge. Everyone is kind of already doing that. To get to the next level our customers are recently telling us it is only by finding key insights in data that they're gonna beat out the competition and stay relevant. I mean, look what Amazon and Netflix have done to the industries that, they weren't as data driven, and have that kind of agility around data. So everybody wants to do the same thing. So CIOs, CDOs, chief data officers, we're seeing them crop up more and more and being more and more empowered in the organization. Because it's seen as central to hitting revenue targets and making an impact, which is what customers want to do. And I mentioned CISOs as well with the question that you asked, Keith, about security. >> The CISOs, the chief information security officers. >> Aaron: Yeah, absolutely. Yeah, absolutely, so I think usually often a CISO will report into a CIO, often you see it as adjacent to them, there's somebody who needs to have the confidence, as they do, in Alation's process of mirroring what's in the data source, not introducing security holes. Potentially even taking a step forward and saying, as I implement GDPR and other policies, how do I use a comprehensive automated inventory like Alations to make sure that process isn't just started but actually finished and avoid the fines and the adverse events. We absolutely see across the C-suite a lot of interest. >> So let's go one step below the CIO, and I think the CIO understands this. This data is the new oil. Very, very straightforward. But now you're getting into the enterprise architect, the VP of infrastructure, and they have to implement these technologies. What have been some of the rewards and challenges with those conversations? >> That's a great question. Right, so here at AWS Reinvent we have a very technical audience, very infrastructure minded. Those are folks that we love to engage with, but our primary audience is the business. >> Keith: Right. >> Right. And so I think what's interesting is, the problem we solve for the more infrastructure-minded executives is how do I deal with these business users? How do I turn this relationship that feels adversarial, where they're putting strain on my system, they're upset about cost overruns, we don't speak the same language with the same values. Alation can be a great bridge. Because we do all of this automated extraction and tying to the sources where they are, and kind of meet the industry people where they live, but then can communicate the value in a clean interface that demonstrates real business ROI to the business. So we can kid of be an ambassador between those sides of the customer. >> I love that, being an ambassador. Aaron, your passion for Alation, what you do, your engagement with customers is palpable. So we thank you for joining us on theCUBE, and wish you guys the best of luck with what you're doing here at AWS Reinvent. >> Lisa, thank you so much for having me. >> Lisa: Awesome. >> Keith: Great job, Aaron. >> Thank you for watching. We are live at AWS Reinvent 2017 with 42,000 other people. I'm Lisa Martin, for my cohost Keith Townsend and Aaron Kalb, stick around. We'll be right back.