>> Prior to the pandemic, organizations were largely optimized for efficiency as the best path to bottom line profits. Many CIOs tell theCUBE privately that they were caught off guard by the degree to which their businesses required greater resiliency beyond their somewhat cumbersome disaster recovery processes. And the lack of that business resilience has actually cost firms because they were unable to respond to changing market forces. And certainly, we've seen this dynamic with supply chain challenges. And there's a little doubt we're also seeing it in the area of cybersecurity generally, and data recovery specifically. Over the past 30 plus months, the rapid adoption of cloud to support remote workers and build in business resilience had the unintended consequences of expanding attack vectors, which brought an escalation of risk from cybercrime. While security in the public cloud is certainly world class, the result of multicloud has brought with it multiple shared responsibility models, multiple ways of implementing security policies across clouds and on-prem. And at the end of the day, more, not less, . But there's a positive side to this story. The good news is that public policy, industry collaboration and technology innovation is moving fast to accelerate data protection and cybersecurity strategies with a focus on modernizing infrastructure, securing the digital supply chain, and very importantly, simplifying the integration of data protection and cybersecurity. Today, there's heightened awareness that the world of data protection is not only an adjacency to, but is becoming a fundamental component of cybersecurity strategies. In particular, in order to build more resilience into a business, data protection people, technologies and processes must be more tightly coordinated with security operations. Hello, and welcome to "The Future of Multicloud Data Protection" made possible by Dell in collaboration with theCUBE. My name is Dave Vellante and I'll be your host today. In this segment, we welcome into theCUBE two senior executives from Dell who will share details on new technology announcements that directly address these challenges. Jeff Boudreau is the President and General Manager of Dell's Infrastructure Solutions Group, ISG, and he's going to share his perspectives on the market and the challenges he's hearing from customers. And we're going to ask Jeff to double click on the messages that Dell is putting into the marketplace and give us his detailed point of view on what it means for customers. Now, Jeff is going to be joined by Travis Vigil. Travis is the Senior Vice-President of Product Management for ISG at Dell Technologies, and he's going to give us details on the products that are being announced today and go into the hard news. Now, we're also going to challenge our guests to explain why Dell's approach is unique and different in the marketplace. Thanks for being with us. Let's get right into it. (upbeat music) We're here with Jeff Boudreau and Travis Vigil, and we're going to dig into the details about Dell's big data protection announcement. Guys, good to see you. Thanks for coming in. >> Good to see you. Thank you for having us. >> You're very welcome. Alright, let's start off Jeff, with the high level. You know, I'd like to talk about the customer, what challenges they're facing? You're talking to customers all the time. What are they telling you? >> Sure, as you know, we spend a lot of time with our customers, specifically listening, learning, understanding their use cases, their pain points within their specific environments. They tell us a lot. No surprise to any of us that data is a key theme that they talk about. It's one of their most important assets. They need to extract more value from that data to fuel their business models, their innovation engines, their competitive edge. So, they need to make sure that that data is accessible, it's secure and its recoverable, especially in today's world with the increased cyber attacks. >> Okay, so maybe we could get into some of those challenges. I mean, when you talk about things like data sprawl, what do you mean by that? What should people know? >> Sure, so for those big three themes, I'd say, you have data sprawl, which is the big one, which is all about the massive amounts of data. It's the growth of that data, which is growing at unprecedented rates. It's the gravity of that data and the reality of the multicloud sprawl. So stuff is just everywhere, right? Which increases that surface as attack space for cyber criminals. >> And by gravity, you mean the data's there and people don't want to move it. >> It's everywhere, right? And so when it lands someplace, think Edge, Core or Cloud, it's there. And it's something we have to help our customers with. >> Okay, so it's nuanced 'cause complexity has other layers. What are those layers? >> Sure. When we talk to our customers, they tell us complexity is one of their big themes. And specifically it's around data complexity. We talked about that growth and gravity of the data. We talk about multicloud complexity and we talk about multicloud sprawl. So multiple vendors, multiple contracts, multiple tool chains, and none of those work together in this multicloud world. Then that drives their security complexity. So, we talk about that increased attack surface. But this really drives a lot of operational complexity for their teams. Think about we're lacking consistency through everything. So people, process, tools, all that stuff, which is really wasting time and money for our customers. >> So, how does that affect the cyber strategies and the, I mean, I've often said the Cisco, now they have this shared responsibility model. They have to do that across multiple clouds. Every cloud has its own security policies and frameworks and syntax. So, maybe you could double click on your perspective on that. >> Sure. I'd say the big challenge customers have seen, it's really inadequate cyber resiliency and specifically, they're feeling very exposed. And today as the world with cyber attacks being more and more sophisticated, if something goes wrong, it is a real challenge for them to get back up and running quickly. And that's why this is such a big topic for CEOs and businesses around the world. You know, it's funny. I said this in my open. I think that prior to the pandemic businesses were optimized for efficiency, and now they're like, "Wow, we have to actually put some headroom into the system to be more resilient." You know, are you hearing that? >> Yeah, we absolutely are. I mean, the customers really, they're asking us for help, right? It's one of the big things we're learning and hearing from them. And it's really about three things. One's about simplifying IT. Two, it's really helping them to extract more value from their data. And then the third big piece is ensuring their data is protected and recoverable regardless of where it is going back to that data gravity and that very, you know, the multicloud world. Just recently, I don't know if you've seen it, but the Global Data Protected, excuse me, the Global Data Protection Index. >> GDPI. >> Yes. Jesus. >> Not to be confused with GDPR. >> Actually, that was released today and confirms everything we just talked about around customer challenges. But also it highlights at an importance of having a very cyber, a robust cyber resilient data protection strategy. >> Yeah, I haven't seen the latest, but I want to dig into it. I think this, I've done this many, many years in a row. I'd like to look at the time series and see how things have changed. All right. At a high level, Jeff, can you kind of address why Dell, from your point of view is best suited? >> Sure. So, we believe there's a better way or a better approach on how to handle this. We think Dell is uniquely positioned to help our customers as a one stop shop, if you will, for that cyber resilient multicloud data protection solution and needs. We take a modern, a simple and resilient approach. >> What does that mean? What do you mean by modern? >> Sure. So modern, we talk about our software defined architecture. Right? It's really designed to meet the needs not only of today, but really into the future. And we protect data across any cloud and any workload. So, we have a proven track record doing this today. We have more than 1,700 customers that trust us to protect more than 14 exabytes of their data in the cloud today. >> Okay, so you said modern, simple and resilient. What do you mean by simple? >> Sure. We want to provide simplicity everywhere, going back to helping with the complexity challenge. And that's from deployment to consumption, to management and support. So, our offers will deploy in minutes. They are easy to operate and use, and we support flexible consumption models for whatever the customer may desire. So, traditional subscription or as a service. >> And when you talk about resilient, I mean, I put forth that premise, but it's hard because people say, "Well, that's going to cost us more. Well, it may, but you're going to also reduce your risk." So, what's your point of view on resilience? >> Yeah, I think it's something all customers need. So, we're going to be providing a comprehensive and resilient portfolio of cyber solutions that are secure by design. And we have some unique capabilities and a combination of things like built in immutability, physical and logical isolation. We have intelligence built in with AI part recovery. And just one, I guess fun fact for everybody is we have, our cyber vault is the only solution in the industry that is endorsed by Sheltered Harbor that meets all the needs of the financial sector. >> So it's interesting when you think about the NIST framework for cybersecurity. It's all about about layers. You're sort of bringing that now to data protection. >> Jeff: Correct. Yeah. >> All right. In a minute, we're going to come back with Travis and dig into the news. We're going to take a short break. Keep it right there. (upbeat music) (upbeat adventurous music) Okay, we're back with Jeff and Travis Vigil to dig deeper into the news. Guys, again, good to see you. Travis, if you could, maybe you, before we get into the news, can you set the business context for us? What's going on out there? >> Yeah. Thanks for that question, Dave. To set a little bit of the context, when you look at the data protection market, Dell has been a leader in providing solutions to customers for going on nearly two decades now. We have tens of thousands of people using our appliances. We have multiple thousands of people using our latest modern, simple PowerProtect Data Manager Software. And as Jeff mentioned, we have, 1,700 customers protecting 14 exabytes of data in the public clouds today. And that foundation gives us a unique vantage point. We talked to a lot of customers and they're really telling us three things. They want simple solutions. They want us to help them modernize. And they want us to add as the highest priority, maintain that high degree of resiliency that they expect from our data protection solutions. So, that's the backdrop to the news today. And as we go through the news, I think you'll agree that each of these announcements deliver on those pillars. And in particular, today we're announcing the PowerProtect Data Manager Appliance. We are announcing PowerProtect Cyber Recovery Enhancements, and we are announcing enhancements to our APEX Data Storage Services. >> Okay, so three pieces. Let's dig to that. It's interesting, appliance, everybody wants software, but then you talk to customers and they're like, "Well, we actually want appliances because we just want to put it in and it works." >> Travis: (laughs) Right. >> It performs great. So, what do we need to know about the appliance? What's the news there? >> Well, you know, part of the reason I gave you some of those stats to begin with is that we have this strong foundation of experience, but also intellectual property components that we've taken that have been battle tested in the market. And we've put them together in a new simple, integrated appliance that really combines the best of the target appliance capabilities we have with that modern, simple software. And we've integrated it from the, you know, sort of taking all of those pieces, putting them together in a simple, easy to use and easy to scale interface for customers. >> So, the premise that I've been putting forth for months now, probably well over a year, is that data protection is becoming an extension of your cybersecurity strategies. So, I'm interested in your perspective on cyber recovery. Your specific news that you have there. >> Yeah, you know, we are in addition to simplifying things via the appliance, we are providing solutions for customers no matter where they're deploying. And cyber recovery, especially when it comes to cloud deployments, is an increasing area of interest and deployment that we see with our customers. So, what we're announcing today is that we're expanding our cyber recovery services to be available in Google Cloud. With this announcement, it means we're available in all three of the major clouds and it really provides customers the flexibility to secure their data no matter if they're running on-premises, in Acolo, at the Edge, in the public cloud. And the other nice thing about this announcement is that you have the ability to use Google Cloud as a cyber recovery vault that really allows customers to isolate critical data and they can recover that critical data from the vault back to on-premises or from that vault back to running their cyber protection or their data protection solutions in the public cloud. >> I always invoke my favorite Matt Baker here. "It's not a zero sum game", but this is a perfect example where there's opportunities for a company like Dell to partner with the public cloud provider. You've got capabilities that don't exist there. You've got the on-prem capabilities. We could talk about Edge all day, but that's a different topic. Okay, so my other question Travis, is how does this all fit into APEX? We hear a lot about APEX as a service. It's sort of the new hot thing. What's happening there? What's the news around APEX? >> Yeah, we've seen incredible momentum with our APEX solutions since we introduced data protection options into them earlier this year. And we're really building on that momentum with this announcement being providing solutions that allow customers to consume flexibly. And so, what we're announcing specifically is that we're expanding APEX Data Storage Services to include a data protection option. And it's like with all APEX offers, it's a pay-as-you-go solution. Really streamlines the process of customers purchasing, deploying, maintaining and managing their backup software. All a customer really needs to do is specify their base capacity. They specify their performance tier. They tell us do they want a one year term or a three year term and we take it from there. We get them up and running so they can start deploying and consuming flexibly. And as with many of our APEX solutions, it's a simple user experience all exposed through a unified APEX Console. >> Okay, so it's, you're keeping it simple, like I think large, medium, small. You know, we hear a lot about T-shirt sizes. I'm a big fan of that 'cause you guys should be smart enough to figure out, you know, based on my workload, what I need. How different is this? I wonder if you guys could address this. Jeff, maybe you can start. >> Sure, I'll start and then- >> Pitch me. >> You know, Travis, you jump in when I screw up here. >> Awesome. >> So, first I'd say we offer innovative multicloud data protection solutions. We provide that deliver performance, efficiency and scale that our customers demand and require. We support as Travis said, all the major public clouds. We have a broad ecosystem of workload support and I guess the great news is we're up to 80% more cost effective than any of the competition. >> Dave: 80%? >> 80% >> Hey, that's a big number. All right, Travis, what's your point of view on this? >> Yeah, I think number one, end-to-end data protection. We are that one stop shop that I talked about, whether it's a simplified appliance, whether it's deployed in the cloud, whether it's at the Edge, whether it's integrated appliances, target appliances, software. We have solutions that span the gamut as a service. I mentioned the APEX Solution as well. So really, we can provide solutions that help support customers and protect them, any workload, any cloud, anywhere that data lives. Edge, Core to Cloud. The other thing that we hear as a big differentiator for Dell, and Jeff touched on on this a little bit earlier, is our Intelligent Cyber Resiliency. We have a unique combination in the market where we can offer immutability or protection against deletion as sort of that first line of defense. But we can also offer a second level of defense, which is isolation, talking about data vaults or cyber vaults and cyber recovery. And more importantly, the intelligence that goes around that vault. It can look at detecting cyber attacks. It can help customers speed time to recovery. And really provides AI and ML to help early diagnosis of a cyber attack and fast recovery should a cyber attack occur. And if you look at customer adoption of that solution, specifically in the cloud, we have over 1300 customers utilizing PowerProtect Cyber Recovery. >> So, I think it's fair to say that your portfolio has obviously been a big differentiator. Whenever I talk to your finance team, Michael Dell, et cetera, that end-to-end capability, that your ability to manage throughout the supply chain. We actually just did an event recently with you guys where you went into what you're doing to make infrastructure trusted. And so my take on that is you, in a lot of respects, you're shifting the client's burden to your R&D. now they have a lot of work to do, so it's not like they can go home and just relax. But that's a key part of the partnership that I see. Jeff, I wonder if you could give us the final thoughts. >> Sure. Dell has a long history of being a trusted partner within IT, right? So, we have unmatched capabilities. Going back to your point, we have the broadest portfolio. We're a leader in every category that we participate in. We have a broad deep breadth of portfolio. We have scale. We have innovation that is just unmatched. Within data protection itself, we are the trusted market leader. No if, ands or buts. We're number one for both data protection software in appliances per IDC and we were just named for the 17th consecutive time the leader in the Gartner Magic Quadrant. So, bottom line is customers can count on Dell. >> Yeah, and I think again, we're seeing the evolution of data protection. It's not like the last 10 years. It's really becoming an adjacency and really, a key component of your cyber strategy. I think those two parts of the organization are coming together. So guys, really appreciate your time. Thanks for coming. >> Thank you, sir. >> Dave. >> Travis, good to see you. All right, in a moment I'm going to come right back and summarize what we learned today, what actions you can take for your business. You're watching "The Future of Multicloud Data Protection" made possible by Dell in collaboration with theCUBE, your leader in enterprise and emerging tech coverage. Right back. >> Advertiser: In our data-driven world, protecting data has never been more critical. To guard against everything from cyber incidents to unplanned outages, you need a cyber resilient multicloud data protection strategy. >> It's not a matter of if you're going to get hacked, it's a matter of when. And I want to know that I can recover and continue to recover each day. >> It is important to have a cyber security and a cyber resiliency plan in place because the threat of cyber attack are imminent. >> Advertiser: PowerProtect Data Manager from Dell Technologies helps deliver the data protection and security confidence you would expect from a trusted partner and market leader. >> We chose PowerProtect Data Manager because we've been a strategic partner with Dell Technologies for roughly 20 years now. Our partnership with Dell Technologies has provided us with the ability to scale and grow as we've transitioned from 10 billion in assets to 20 billion. >> Advertiser: With PowerProtect Data Manager, you can enjoy exceptional ease of use to increase your efficiency and reduce costs. >> I'd installed it by myself, learn it by myself. It was very intuitive. >> While restoring your machine with PowerProtect Data Manager is fast, we can fully manage PowerProtect through the center. We can recover a whole machine in seconds. >> Instructor: Data Manager offers innovation such as transparent snapshots to simplify virtual machine backups, and it goes beyond backup and restore to provide valuable insights into protected data, workloads and VMs. >> In our previous environment, it would take anywhere from three to six hours a night to do a single backup of each VM. Now, we're backing up hourly and it takes two to three seconds with the transparent snapshots. >> Advertiser: With PowerProtect's Data Manager, you get the peace of mind knowing that your data is safe and available whenever you need it. >> Data is extremely important. We can't afford to lose any data. We need things just to work. >> Advertiser: Start your journey to modern data protection with Dell PowerProtect's Data Manager. Visit dell.com/powerprotectdatamanager >> We put forth the premise in our introduction that the worlds of data protection in cybersecurity must be more integrated. We said that data recovery strategies have to be built into security practices and procedures and by default, this should include modern hardware and software. Now, in addition to reviewing some of the challenges that customers face, which have been pretty well documented, we heard about new products that Dell Technologies is bringing to the marketplace that specifically address these customer concerns. And there were three that we talked about today. First, the PowerProtect Data Manager Appliance, which is an integrated system taking advantage of Dell's history in data protection, but adding new capabilities. And I want to come back to that in a moment. Second is Dell's PowerProtect Cyber Recovery for Google Cloud platform. This rounds out the big three public cloud providers for Dell, which joins AWS and Azure support. Now finally, Dell has made its target backup appliances available in APEX. You might recall, earlier this year we saw the introduction from Dell of APEX Backup Services and then in May at Dell Technologies World, we heard about the introduction of APEX Cyber Recovery Services. And today, Dell is making its most popular backup appliances available in APEX. Now, I want to come back to the PowerProtect Data Manager Appliance because it's a new integrated appliance and I asked Dell off camera, "Really what is so special about these new systems and what's really different from the competition?" Because look, everyone offers some kind of integrated appliance. So, I heard a number of items. Dell talked about simplicity and efficiency and containers and Kubernetes. So, I kind of kept pushing and got to what I think is the heart of the matter in two really important areas. One is simplicity. Dell claims that customers can deploy the system in half the time relative to the competition. So, we're talking minutes to deploy, and of course that's going to lead to much simpler management. And the second real difference I heard was backup and restore performance for VMware workloads. In particular, Dell has developed transparent snapshot capabilities to fundamentally change the way VMs are protected, which leads to faster backup and restores with less impact on virtual infrastructure. Dell believes this new development is unique in the market and claims that in its benchmarks, the new appliance was able to back up 500 virtual machines in 47% less time compared to a leading competitor. Now, this is based on Dell benchmarks, so hopefully these are things that you can explore in more detail with Dell to see if and how they apply to your business. So if you want more information, go to the Data Protection Page at dell.com. You can find that at dell.com/dataprotection. And all the content here and other videos are available on demand at theCUBE.net. Check out our series on the blueprint for trusted infrastructure, it's related and has some additional information. And go to siliconangle.com for all the news and analysis related to these and other announcements. This is Dave Vellante. Thanks for watching "The Future of Multicloud Protection" made possible by Dell, in collaboration with theCUBE, your leader in enterprise and emerging tech coverage. (upbeat music)

>> Live from the MIT campus in Cambridge, Massachusetts, it's theCUBE, covering the 12th Annual MIT Chief Data Officer and Information Quality Symposium. Brought to you by SiliconANGLE Media. >> Welcome back to theCUBE's coverage of MIT CDOIQ here in Cambridge, Massachusetts. I'm your host Rebecca Knight along with my cohost Peter Burris. We have two guests on this segment. We have Cortnie Abercrombie, she is the founder of the nonprofit AI Truth, and Carl Gerber, who is the managing partner at Global Data Analytics Leaders. Thanks so much for coming on theCUBE Cortnie and Carl. >> Thank you. >> Thank you. >> So I want to start by just having you introduce yourselves to our viewers, what you do. So tell us a little bit about AI Truth, Cortnie. >> So this was born out of a passion. As I, the last gig I had at IBM, everybody knows me for chief data officer and what I did with that, but the more recent role that I had was developing custom offerings for Fortune 500 in the AI solutions area, so as I would go meet and see different clients, and talk with them and start to look at different processes for how you implement AI solutions, it became very clear that not everybody is attuned, just because they're the ones funding the project or even initiating the purpose of the project, the business leaders don't necessarily know how these things work or run or what can go wrong with them. And on the flip side of that, we have very ambitious up-and-comer-type data scientists who are just trying to fulfill the mission, you know, the talent at hand, and they get really swept up in it. To the point where you can even see that data's getting bartered back and forth with any real governance over it or policies in place to say, "Hey, is that right? Should we have gotten that kind of information?" Which leads us into things like the creepy factor. Like, you know target (laughs) and some of these cases that are well-known. And so, as I saw some of these mistakes happening that were costing brand reputation, our return on investment, or possibly even creating opportunities for risk for the companies and for the business leaders, I felt like someone's got to take one for the team here and go out and start educating people on how this stuff actually works, what the issues can be and how to prevent those issues, and then also what do you do when things do go wrong, how do you fix it? So that's the mission of AI Truth and I have a book. Yes, power to the people, but you know really my main concern was concerned individuals, because I think we've all been affected when we've sent and email and all of a sudden we get a weird ad, and we're like, "Hey, what, they should not, is somebody reading my email?" You know, and we feel this, just, offense-- >> And the answer is yes. >> Yes, and they are, they are. So I mean, we, but we need to know because the only way we can empower ourselves to do something is to actually know how it works. So, that's what my missions is to try and do. So, for the concerned individuals out there, I am writing a book to kind of encapsulate all the experiences that I had so people know where to look and what they can actually do, because you'll be less fearful if you know, "Hey, I can download DuckDuckGo for my browser, or my search engine I mean, and Epic for my browser, and some private, you know, private offerings instead of the typical free offerings. There's not an answer for Facebook yet though. >> So, (laughs) we'll get there. Carl, tell us a little bit about Global Data Analytics Leaders. >> So, I launched Analytics Leaders and CDO Coach after a long career in corporate America. I started building an executive information system when I was in the military for a four-star commander, and I've really done a lot in data analytics throughout my career. Most recently, starting a CDO function at two large multinational companies in leading global transformation programs. And, what I've experienced is even though the industries may vary a little bit, the challenges are the same and the patterns of behavior are the same, both the good and bad behavior, bad habits around the data. And, through the course of my career, I've developed these frameworks and playbooks and just ways to get a repeatable outcome and bring these new technologies like machine learning to bear to really overcome the challenges that I've seen. And what I've seen is a lot of the current thinking is we're solving these data management problems manually. You know, we all hear the complaints about the people who are analysts and data scientists spending 70, 80% of their time being a data gatherer and not really generating insight from the data itself and making it actionable. Well, that's why we have computer systems, right? But that large-scale technology in automation hasn't really served us well, because we think in silos, right? We fund these projects based on departments and divisions. We acquire companies through mergers and acquisitions. And the CDO role has emerged because we need to think about this, all the data that an enterprise uses, horizontally. And with that, I bring a high degree of automation, things like machine learning, to solve those problems. So, I'm now bottling that and advising my clients. And at the same time, the CDO role is where the CIO role was 20 years ago. We're really in it's infancy, and so you see companies define it differently, have different expectations. People are filling the roles that may have not done this before, and so I provide the coaching services there. It's like a professional golfer who has a swing coach. So I come in and I help the data executives with upping their game. >> Well, it's interesting, I actually said the CIO role 40 years ago. But, here's why. If we look back in the 1970s, hardcore financial systems were made possible by the technology which allowed us to run businesses like a portfolio: Jack Welch, the GE model. That was not possible if you didn't have a common asset management system, if you didn't have a common cached management system, etc. And so, when we started creating those common systems, we needed someone that could describe how that shared asset was going to be used within the organization. And we went from the DP manager in HR, the DP manager within finance, to the CIO. And in many respects, we're doing the same thing, right? We're talking about data in a lot of different places and now the business is saying, "We can bring this data together in new and interesting ways into more a shared asset, and we need someone that can help administer that process, and you know, navigate between different groups and different needs and whatnot." Is that kind of what you guys are seeing? >> Oh yeah. >> Yeah. >> Well you know once I get to talking (laughs). For me, I can going right back to the newer technologies like AI and IOT that are coming from externally into your organization, and then also the fact that we're seeing bartering at an unprec... of data at an unprecedented level before. And yet, what the chief data officer role originally did was look at data internally, and structured data mostly. But now, we're asking them to step out of their comfort zone and start looking at all these unknown, niche data broker firms that may or may not be ethical in how they're... I mean, I... look I tell people, "If you hear the word scrape, you run." No scraping, we don't want scraped data, no, no, no (laugh). But I mean, but that's what we're talking about-- >> Well, what do you mean by scraped data, 'cause that's important? >> Well, this is a well-known data science practice. And it's not that... nobody's being malicious here, nobody's trying to have a malintent, but I think it's just data scientists are just scruffy, they roll up their sleeves and they get data however they can. And so, the practice emerged. Look, they're built off of open-source software and everything's free, right, for them, for the most part? So they just start reading in screens and things that are available that you could see, they can optical character read it in, or they can do it however without having to have a subscription to any of that data, without having to have permission to any of that data. It's, "I can see it, so it's mine." But you know, that doesn't work in candy stores. We can't just go, or jewelry stores in my case, I mean, you can't just say, "I like that diamond earring, or whatever, I'm just going to take it because I can see it." (laughs) So, I mean, yeah we got to... that's scraping though. >> And the implications of that are suddenly now you've got a great new business initiative and somebody finds out that you used their private data in that initiative, and now they've got a claim on that asset. >> Right. And this is where things start to get super hairy, and you just want to make sure that you're being on the up-and-up with your data practices and you data ethics, because, in my opinion, 90% of what's gone wrong in AI or the fear factor of AI is that your privacy's getting violated and then you're labeled with data that you may or may not know even exists half the time. I mean. >> So, what's the answer? I mean as you were talking about these data scientists are scrappy, scruffy, roll-up-your-sleeves kind of people, and they are coming up with new ideas, new innovations that sometimes are good-- >> Oh yes, they are. >> So what, so what is the answer? Is this this code of ethics? Is it a... sort of similar to a Hippocratic Oath? I mean how would you, what do you think? >> So, it's a multidimensional problem. Cortnie and I were talking earlier that you have to have more transparency into the models you're creating, and that means a significant validation process. And that's where the chief data officer partners with folks in risk and other areas and the data science team around getting more transparency and visibility into what's the data that's feeding into it? Is it really the authoritative data of the company? And as Cortnie points out, do we even have the rights to that data that's feeding our models? And so, by bringing that transparency and a little more validation before you actually start making key, bet-the-business decisions on the outcomes of these models, you need to look at how you're vetting them. >> And the vetting process is part technology, part culture, part process, it goes back to that people process technology trying. >> Yeah, absolutely, know where your data came from. Why are you doing this model? What are you doing to do with the outcomes? Are you actually going to do something with it or are you going to ignore it? Under what conditions will you empower a decision-maker to use the information that is the output of the model? A lot of these things, you have to think through when you want to operationalize it. It's not just, "I'm going to go get a bunch of data wherever I can, I put a model together. Here, don't you like the results?" >> But this is Silicon Valley way, right? An MVP for everything and you just let it run until... you can't. >> That's a great point Cortnie (laughs) I've always believed, and I want to test this with you, we talk about people process technology about information, we never talk about people process technology and information of information. There's a manner of respects what we're talking about is making explicit the information about... information, the metadata, and how we manage that and how we treat that, and how we defuse that, and how we turn that, the metadata itself, into models to try to govern and guide utilization of this. That's especially important in AI world, isn't it? >> I start with this. For me, it's simple, I mean, but everything he said was true. But, I try to keep it to this: it's about free will. If I said you can do that with my data, to me it's always my data. I don't care if it's on Facebook, I don't care where it is and I don't care if it's free or not, it's still my data. Even if it's X23andMe, or 23andMe, sorry, and they've taken the swab, or whether it's Facebook or I did a google search, I don't care, it's still my data. So if you ask me if it's okay to do a certain type of thing, then maybe I will consent to that. But I should at least be given an option. And no, be given the transparency. So it's all about free will. So in my mind, as long as you're always providing some sort of free will (laughs), the ability for me to having a decision to say, "Yes, I want to participate in that," or, "Yes, you can label me as whatever label I'm getting, Trump or a pro-Hillary or Obam-whatever, name whatever issue of the day is," then I'm okay with that as long as I get a choice. >> Let's go back to it, I want to build on that if I can, because, and then I want to ask you a question about it Carl, the issue of free will presupposes that both sides know exactly what's going into the data. So for example, if I have a medical procedure, I can sit down on that form and I can say, "Whatever happens is my responsibility." But if bad things happen because of malfeasance, guess what? That piece of paper's worthless and I can sue. Because the doctor and the medical provider is supposed to know more about what's going on than I do. >> Right. >> Does the same thing exist? You talked earlier about governance and some of the culture imperatives and transparency, doesn't that same thing exist? And I'm going to ask you a question: is that part of your nonprofit is to try to raise the bar for everybody? But doesn't that same notion exist, that at the end of the day, you don't... You do have information asymmetries, both sides don't know how the data's being used because of the nature of data? >> Right. That's why you're seeing the emergence of all these data privacy laws. And so what I'm advising executives and the board and my clients is we need to step back and think bigger about this. We need to think about as not just GDPR, the European scope, it's global data privacy. And if we look at the motivation, why are we doing this? Are we doing it just because we have to be regulatory-compliant 'cause there's a law in the books, or should we reframe it and say, "This is really about the user experience, the customer experience." This is a touchpoint that my customers have with my company. How transparent should I be with what data I have about you, how I'm using it, how I'm sharing it, and is there a way that I can turn this into a positive instead of it's just, "I'm doing this because I have to for regulatory-compliance." And so, I believe if you really examine the motivation and look at it from more of the carrot and less of the stick, you're going to find that you're more motivated to do it, you're going to be more transparent with your customers, and you're going to share, and you're ultimately going to protect that data more closely because you want to build that trust with your customers. And then lastly, let's face it, this is the data we want to analyze, right? This is the authenticated data we want to give to the data scientists, so I just flip that whole thing on its head. We do for these reasons and we increase the transparency and trust. >> So Cortnie, let me bring it back to you. >> Okay. >> That presupposes, again, an up-leveling of knowledge about data privacy not just for the executive but also for the consumer. How are you going to do that? >> Personally, I'm going to come back to free will again, and I'm also going to add: harm impacts. We need to start thinking impact assessments instead of governance, quite frankly. We need to start looking at if I, you know, start using a FICO score as a proxy for another piece of information, like a crime record in a certain district of whatever, as a way to understand how responsible you are and whether or not your car is going to get broken into, and now you have to pay more. Well, you're... if you always use a FICO score, for example, as a proxy for responsibility which, let's face it, once a data scientist latches onto something, they share it with everybody 'cause that's how they are, right? They love that and I love that about them, quite frankly. But, what I don't like is it propagates, and then before you know it, the people who are of lesser financial means, it's getting propagated because now they're going to be... Every AI pricing model is going to use FICO score as a-- >> And they're priced out of the market. >> And they're priced out of the market and how is that fair? And there's a whole group, I think you know about the Fairness Accountability Transparency group that, you know, kind of watch dogs this stuff. But I think business leaders as a whole don't really think through to that level like, "If I do this, then this this and this could incur--" >> So what would be the one thing you could say if, corporate America's listening. >> Let's do impact. Let's do impact assessments. If you're going to cost someone their livelihood, or you're going to cost them thousands of dollars, then let's put more scrutiny, let's put more government validation. To your point, let's put some... 'cause not everything needs the nth level. Like, if I present you with a blue sweater instead of a red sweater on google or whatever, (laughs) You know, that's not going to harm you. But it will harm you if I give you a teacher assessment that's based on something that you have no control over, and now you're fired because you've been laid off 'cause your rating was bad. >> This is a great conversation. Let me... Let me add something different, 'cause... Or say it a different way, and tell me if you agree. In many respects, it's: Does this practice increase inclusion or does this practice decrease inclusion? This is not some goofy, social thing, this is: Are you making your market bigger or are you making your market smaller? Because the last thing you want is that the participation by people ends with: You can't play because of some algorithmic response we had. So maybe the question of inclusion becomes a key issue. Would you agree with that? >> I do agree with it, and I still think there's levels even to inclusion. >> Of course. >> Like, you know, being a part of the blue sweater club versus the (laughs) versus, "I don't want to be a convict," you know, suddenly because of some record you found, or association with someone else. And let's just face it, a lot of these algorithmic models do do these kinds of things where they... They use n+1, you know, a lot... you know what I'm saying. And so you're associated naturally with the next person closest to you, and that's not always the right thing to do, right? So, in some ways, and so I'm positing just little bit of a new idea here, you're creating some policies, whether you're being, and we were just talking about this, but whether you're being implicit about them or explicit, more likely you're being implicit because you're just you're summarily deciding. Well, okay, I have just decided in the credit score example, that if you don't have a good credit threshold... But where in your policies and your corporate policy did it ever say that people of lesser financial means should be excluded from being able to have good car insurance for... 'cause now, the same goes with like Facebook. Some people feel like they're going to have to opt of of life, I mean, if they don't-- >> (laughs) Opt out of life. >> I mean like, seriously, when you think about grandparents who are excluded, you know, out in whatever Timbuktu place they live, and all their families are somewhere else, and the only way that they get to see is, you know, on Facebook. >> Go back to the issue you raised earlier about "Somebody read my email," I can tell you, as a person with a couple of more elderly grandparents, they inadvertently shared some information with me on Facebook about a health condition that they had. You know how grotesque the response of Facebook was to that? And, it affected me to because they had my name in it. They didn't know any better. >> Sometimes there's a stigma. Sometimes things become a stigma as well. There's an emotional response. When I put the article out about why I left IBM to start this new AI Truth nonprofit, the responses I got back that were so immediate were emotional responses about how this stuff affects people. That they're scared of what this means. Can people come after my kids or my grandkids? And if you think about how genetic information can get used, you're not just hosing yourself. I mean, breast cancer genes, I believe, aren't they, like... They run through families, so, I-- >> And they're pretty well-understood. >> If someone swabs my, and uses it and swaps it with other data, you know, people, all of a sudden, not just me is affected, but my whole entire lineage, I mean... It's hard to think of that, but... it's true (laughs). >> These are real life and death... these are-- >> Not just today, but for the future. And in many respects, it's that notion of inclusion... Going back to it, now I'm making something up, but not entirely, but going back to some of the stuff that you were talking about, Carl, the decisions we make about data today, we want to ensure that we know that there's value in the options for how we use that data in the future. So, the issue of inclusion is not just about people, but it's also about other activities, or other things that we might be able to do with data because of the nature of data. I think we always have to have an options approach to thinking about... as we make data decisions. Would you agree with that? Yes, because you know, data's not absolute. So, you can measure something and you can look at the data quality, you can look at the inputs to a model, whatever, but you still have to have that human element of, "Are you we doing the right thing?" You know, the data should guide us in our decisions, but I don't think it's ever an absolute. It's a range of options, and we chose this options for this reason. >> Right, so are we doing the right thing and do no harm too? Carl, Cortnie, we could talk all day, this has been a really fun conversation. >> Oh yeah, and we have. (laughter) >> But we're out of time. I'm Rebecca Knight for Peter Burris, we will have more from MIT CDOIQ in just a little bit. (upbeat music)

>> Live from San Jose, in the heart of Silicon Valley, it's theCube. Covering Datawork Summit 2018. Brought to you by Hortonworks. >> Welcome back to theCube's live coverage of Dataworks here in San Jose, California. I'm your host, Rebecca Knight along with my cohost James Kobielus. We have with us Vimal Endiran. He is the Global Business Data Group Ecosystem Lead, at Accenture. He's coming to us straight from the Motor City. So, welcome Vimal. >> Thank you, thank you Rebecca. Thank you Jim. Looking forward to talk to you for the next ten minutes. >> So, before the cameras were rolling we were talking about how data veracity and how managers can actually know that the data that they're getting, that they're seeing, is trustworthy. What's your take on that right now? >> So, in the today's age the data is coming at you in a velocity that you never thought about, right. So today, the organizations are gathering data probably in the magnitude of petabytes. This is a new normal. We used to talk about gigs and now it's in petabytes. And the data coming in the form of images, video files, from the edge, you know edge devices, sensors, social media and everything. So, the amount of data, this is becoming the fuel for the new economy, right. So that companies, who can find a way to take advantage and figure out a way to use this data going to have a competitive advantage over their competitors. So, for that purpose, even though it's coming at that volume and velocity doesn't mean it's useful. So the thing is if they can find a way to make the data can be trustworthy, by the organization, and at the same time it's governed and secured. That's what's going to happen. It used to be it's called data quality, we call it when the structure it's okay, everything is maintained in SAP or some system. It's good it's coming to you. But now, you need to take advantage of the tools like machine learning, artificial intelligence, combining these algorithms and tool sets and abilities of people's mind, putting that in there and making it somewhat... Things can happen to itself at the same time it's trustworthy, we have offerings around that Accenture is developing place... It differs from industry to industry. Given the fact if the data coming in is something it's only worth for 15 seconds. After that it has no use other than understanding how to prevent something, from a sense of data. So, we have our offerings putting into place to make the data in a trustworthy, governed, secured, for an organization to use it and help the organization to get there. That's what we are doing. >> The standard user of your tools is it a data steward in the traditional sense or is it a data scientist or data engineer who's trying to, for example, compile a body of training data for use in building and training machine learning models? Do you see those kinds of customers for your data veracity offerings, that customer segment growing? >> Yes. We see both sides pretty much all walk of customers in our life. So, you hit the nail on the head, yes. We do see that type of aspects and also becoming, the data scientists you're also getting another set of people, the citizen data scientist. The people--- >> What is that? That's a controversial term. I've used that term on a number of occasions and a lot of my colleagues and peers in terms of other analysts bat me down and say, "No, that demeans the profession of data science by calling it..." But you tell me what how Accenture's defining that. >> The thing is, it's not demeaning. The fact is to become a citizen data scientist you need the help of data scientists. Basically, every time you need to build a model. And then you feed some data to learn. And then have an outcome to put that out. So you have a data scientist creating algorithms. What a citizen data scientist means, say if I'm not a data scientist, I should be able to take advantage of a model built for my business scenario, feed something data in, whatever I need to feed in, get an output and that program, that tool's going to tell me, go do this or don't do this, kind of things. So I become a data scientist by using a predefined model that's developed by an expert. Minds of many experts together. But rather than me going and hiring hundred experts, I go and buy a model and able to have one person maintain or tweak this model continuously. So, how can I enable that large volume of people by using more models. That's what-- >> If a predictive analytics tool that you would license from whatever vendor. If that includes prebuilt machine learning models for a particular tasks in it does that... Do you as a user of that tool, do you become automatically a citizen data scientist or do you need to do some actual active work with that model or data to live up to the notion of being a citizen data scientist? >> It's a good question. In my mind, I don't want to do it, my job is something else. To make something for the company. So, my job is not creating a model and doing that. My job is, I know my sets of data, I want to feed it in. I want to get the outcome that I can go and say increase my profit, increase my sales. That's what I want to do. So I may become a citizen data scientist without me knowing. I won't even be told that I'm using a model. I will take this set of data, feed it in here, it's going to tell you something. So, our data veracity point of view, we have these models built into some of platforms. That can be a tool from foreign works, taking advantage of the data storage tool or any other... In our own algorithms put in that helps you to create and maintain the data veracity to a scale of, if you say one to five, one is being low, five is being bad, to maintain at the five level. So that's the objective of that. >> So you're democratizing the tools of data science for the rest of us to solve real business problems. >> Right. >> So the data veracity aside, you're saying the user of these tools is doing something to manage, to correct or enhance or augment the data that's used to feed into these prebuilt models to achieve these outcomes? >> Yes. The augmented data, the feed data and the training data it comes out with an outcome to say, go do something. It tells you to perform something or do not perform. It's still an action. Comes out with an action to achieve a target. That's what it's going to be. >> You mention Hortonworks and since we are here at Dataworks and the Hortonworks show, tell us a little bit about your relationship with that company. >> Definitely. So Hortonworks is one of our premiere strategic partners. We've been the number one implementers, the partners for last two years in a row, implementing their technology across many of our clients. From partnership point of view, we have jointly developed offerings. What Accenture is best at, we're very good at industry knowledge. So with our industry knowledge and with their technology together what we're doing is we're creating some offerings that you can take to market. For example, we used to have data warehouses like using Teradata and older technology data warehouses. They're still good but at the same time, people also want to take the structured, unstructured data, images files and able to incorporate into the existing data warehouses. And how I can get the value out of the whole thing together. That's where Hortonworks' type of tools comes to play. So we have developed offerings called Modern Data Warehouse, taking advantage of your legacy systems you have plus this new data coming together and immediately you can create an analytics case, used case to do something. So, we have prebuilt programs and different scripts that take in different types of data. Moving into a data lake, Hortonworks data lake and then use your existing legacy data and all those together help you to create analytics use cases. So we have that called data modernization offering, we have one of that. Then we have-- >> So that's a prebuilt model for a specific vertical industry requirements or a specific business function, predictive analytics, anomaly detection and natural language processing, am I understanding correctly? >> Yes. We have industry based solutions as well but also to begin with, the data supply chain itself. To bring the data into the lake to use it. That's one of the offerings we play-- >> ...Pipeline and prepackaged models and rules and so forth. >> Right, prepackaged data ingestion, transformation, that prepackaged to take advantage with the new data sets along with your legacy data. That's one offering called data modernization offering. That to cloud. So, we can take to cloud. Hortonworks in a cloud it can be a joure, WS, HP, any cloud plus moving data. So that's one type of offering. Today actually we announced another offering jointly with Hortonworks, Atlas and Grainger Tool to help GDPR compliance. >> Will you explain what that tool does specifically to help customers with GDPR points. Does it work out of the box with Hortonworks data stewards studio? >> Well, to me I can get your answers from my colleagues who are much more technical on that but the fact is I can tell you functionally what the tool does is. >> Okay, please. >> So you, today the GDPR is basically, there's account regulations about you need to know about your personal data and you have your own destiny about your personal data. You can call the company and say, "Forget about me." If you are an EU resident. Or say, "Modify my data." They have to do it within certain time frame. If not they get fined. The fine can be up to 4% of the company's... So it's going to be a very large fine. >> Total revenue, yeah. >> So what we do is, basically take this tool. Put it in, working with Hortonworks this Atlas and Granger tool, we can go in and scan your data leak and we can scan at the metadata level and come into showcase. Then you know where is your personal data information about a consumer lies and now I know everything. Because what used to be in a legacy situation, the data originated someplace, somebody takes it and puts a system then somebody else downloads to an X file, somebody will put in an access data base and this kind of things. So now your data's pulling it across, you don't know where that lies. In this case, in the lake we can scan it, put this information, the meta data and the lineage information. Now, you immediately know where the data lies when somebody calls. Rebecca calls and says, "No longer use my information." I exactly know it's stored in this place in this table, in this column, let me go and take it out from here so that Rebecca doesn't exist anymore. Or whoever doesn't exist anymore. So that's the idea behind it. Also, we can catalog the entire data lake and we know not just personal information, other information, everything about other dimensions as well. And we can use it for our business advantage. So that's what we announced today. >> We're almost out of time but I want to finally ask you about talent because this is a pressing issue in Silicon Valley and beyond in really the tech industry, finding the right people, putting them in the right jobs and then keeping them happy there. So recruiting, retaining, what's Accenture's approach? >> This area, talent is the hardest one. >> Yes! >> Thanks to Hortonworks and Hortonworks point of view >> Send them to Detroit where the housing is far less expensive. >> Not a bad idea. >> Exactly! But the fact is-- >> We're both for Detroiters. >> What we did was, Hortonworks, Accenture has access to Hortonworks University, all their educational aspects. So we decided we're going to take that advantage and we going to enhance our talent by bringing the people from our... Retraining the people, taking the people to the new. People who know the legacy data aspects. So take them to see how we take the new world. So then we have a plan to use Hortonworks together the University, the materials and the people help, together we going to train about 500 people in different geos, 500 per piece and also our the development centers in India, Philippines, these places, so we have a larger plan to retrain the legacy into new. So, let's go and get people from out of the college and stuff, start building them from there, from an analyst to a consultant to a technical level and so that's the best way we are doing and actually the group I work with. Our group technology officer Sanjiv Vohra, he's basically in charge of training about 90,000 people on different technologies in and around that space. So the magnet is high but that's our approach to go and try and people and take it to that. >> Are you training them to be well rounded professionals in all things data or are you training them for specific specialties? >> Very, very good question. We do have this call master data architect program, so basically in the different levels after these trainings people go through specially you have to do so many projects, come back have an interview with a panel of people and you get certified, within the company, at certain level. At the master architect level you go and help a customer transform their data transformation, architecture vision where do you want to go to, that level. So we have the program with a university and that's the way we've taken it step by step to people to that level. >> Great. Vimal, thank you so much for coming on theCube. >> Thank you. >> It was really fun talking to you. >> Thank you so much, thank you for having me. Thank you. >> I'm Rebecca Knight for James Kobielus we will have more, well we actually will not be having any more coming up from Dataworks. This has been the Dataworks show. Thank you for tuning in. >> Signing off for now. >> And we'll see you next time.

>> Live from San Jose in the heart of Silicon Valley, it's theCUBE covering DataWorks Summit 2018, brought to you by Hortonworks. >> Welcome back to theCUBE's live coverage of DataWorks Summit here in San Jose, California. I'm your host, Rebecca Knight, along with my co-host, James Kobielus. We're joined by Rob Bearden. He is the CEO of Hortonworks. So thanks so much for coming on theCUBE again, Rob. >> Thank you for having us. >> So you just got off of the keynote on the main stage. The big theme is really about modern data architecture. So we're going to have this modern data architecture. What is it all about? How do you think about it? What's your approach? And how do you walk customers through this process? >> Well, there's a lot of moving parts in enabling a modern data architecture. One of the first steps is what we're trying to do is unlock the siloed transactional applications, and to get that data into a central architecture so you can get real time insights around the inclusive dataset. But what we're really trying to accomplish then within that modern data architecture is to bring all types of data whether it be real time streaming data, whether it be sensor data, IoT data, whether it be data that's coming from a connected core across the network, and to be able to bring all that data together in real time, and give the enterprise the ability to be able to take best in class action so that you get a very prescriptive outcome of what you want. So if we bring that data under management from point of origination and out on the edge, and then have the platforms that move that through its entire lifecycle, and that's our HDF platform, it gives the customer the ability to, after they capture it at the edge, move it, and then have the ability to process it as an event happens, a condition changes, various conditions come together, have the ability to process and take the exact action that you want to see performed against that, and then bring it to rest, and that's where our HDP platform comes into play where then all that data can be aggregated so you can have a holistic insight, and have real time interactions on that data. But then it then becomes about deploying those datasets and workloads on the tier that's most economically and architecturally pragmatic. So if that's on-prem, we make sure that we are architected for that on-prem deployment or private cloud or even across multiple public clouds simultaneously, and give the enterprise the ability to support each of those native environments. And so we think hybrid cloud architecture is really where the vast majority of our customers today and in the future, are going to want to be able to run and deploy their applications and workloads. And that's where our DataPlane Service Offering gives them the ability to have that hybrid architecture and the architectural latitude to move workloads and datasets across each tier transparently to what storage file format that they did or where that application is, and we provide all the tooling to match the complexity from doing that, and then we ensured that it has one common security framework, one common governance through its entire lifecycle, and one management platform to handle that entire lifecycle data. And that's the modern data architecture is to be able to bring all data under management, all types of data under management, and manage that in real time through its lifecycle til it comes at rest and deploy that across whatever architecture tier is most appropriate financially and from a performance on-cloud or prem. >> Rob, this morning at the keynote here in day one at DataWorks San Jose, you presented this whole architecture that you described in the context of what you call hybrid clouds to enable connected communities and with HDP, Hortonworks Data Platform 3.0 is one of the prime announcements, you brought containerization into the story. Could you connect those dots, containerization, connected communities, and HDP 3.0? >> Well, HDP 3.0 is really the foundation for enabling that hybrid architecture natively, and what's it done is it separated the storage from the compute, and so now we have the ability to deploy those workloads via a container strategy across whichever tier makes the most sense, and to move those application and datasets around, and to be able to leverage each tier in the deployment architectures that are most pragmatic. And then what that lets us do then is be able to bring all of the different data types, whether it be customer data, supply chain data, product data. So imagine as an industrial piece of equipment is, an airplane is flying from Atlanta, Georgia to London, and you want to be able to make sure you really understand how well is that each component performing, so that that plane is going to need service when it gets there, it doesn't miss the turnaround and leave 300 passengers stranded or delayed, right? Now with our Connected platform, we have the ability to take every piece of data from every component that's generated and see that in real time, and let the airlines make that real time. >> Delineate essentially. >> And ensure that we know every person that touched it and looked at that data through its entire lifecycle from the ground crew to the pilots to the operations team to the service. Folks on the ground to the reservation agents, and we can prove that if somehow that data has been breached, that we know exactly at what point it was breached and who did or didn't get to see it, and can prevent that because of the security models that we put in place. >> And that relates to compliance and mandates such as the Global Data Protection Regulation GDPR in the EU. At DataWorks Berlin a few months ago, you laid out, Hortonworks laid out, announced a new product called the Data Steward Studio to enable GDPR compliance. Can you give our listeners now who may not have been following the Berlin event a bit of an update on Data Steward Studio, how it relates to the whole data lineage, or set of requirements that you're describing, and then going forward what does Hortonworks's roadmap for supporting the full governance lifecycle for the Connected community, from data lineage through like model governance and so forth. Can you just connect a few dots that will be helpful? >> Absolutely. What's important certainly, driven by GDPR, is the requirement to be able to prove that you understand who's touched that data and who has not had access to it, and that you ensure that you're in compliance with the GDPR regulations which are significant, but essentially what they say is you have to protect the personal data and attributes of that data of the individual. And so what's very important is that you've got to be able to have the systems that not just secure the data, but understand who has the accessibility at any point in time that you've ever maintained that individual's data. And so it's not just about when you've had a transaction with that individual, but it's the rest of the history that you've kept or the multiple datasets that you may try to correlate to try to expand relationship with that customer, and you need to make sure that you can ensure not only that you've secured their data, but then you're protecting and governing who has access to it and when. And as importantly that you can prove in the event of a breach that you had control of that, and who did or did not access it, because if you can't prove any breach, that it was secure, and that no one breached it, who has or access to this not supposed to, you can be opened up for hundreds of thousands of dollars or even multiple millions of dollars of fines just because you can't prove that it was not accessed, and that's what the variety of our platforms, you mentioned Data Studio, is part of. DataPlane is one of the capabilities that gives us the ability. The core engine that does that is Atlas, and that's the open source governance platform that we developed through the community that really drives all the capabilities for governance that moves through each of our products, HDP, HDF, then of course, and DataPlane and Data Studio takes advantage of that and how it moves and replicates data and manages that process for us. >> One of the things that we were talking about before the cameras were rolling was this idea of data driven business models, how they are disrupting current contenders, new rivals coming on the scene all the time. Can you talk a little bit about what you're seeing and what are some of the most exciting and maybe also some of the most threatening things that you're seeing? >> Sure, in the traditional legacy enterprise, it's very procedural driven. You think about classic Encore ERP. It's worked very hard to have a very rigid, very structural procedural order to cash cycle that has not a great deal of flexibility. And it takes through a design process, it builds product, that then you sell product to a customer, and then you service that customer, and then you learn from that transaction different ways to automate or improve efficiencies in their supply chain. But it's very procedural, very linear. And in the new world of connected data models, you want to bring transparency and real time understanding and connectivity between the enterprise, the customer, the product, and the supply chain, and that you can take real time best in practice action. So for example you understand how well your product is performing. Is your customer using it correctly? Are they frustrated with that? Are they using it in the patterns and the frequency that they should be if they are going to expand their use and buy more, and if they're not, how do we engage in that cycle? How do we understand if they're going through a re-review and another buying of something similar that may not be with you for a different reason. And when we have real time visibility to our customer's interaction, understand our product's performance through its entire lifecycle, then we can bring real time efficiency with linking those together with our supply chain into the various relationships we have with our customers. To do that, it requires the modern data architecture, bringing data under management from the point it originates, whether it's from the product or the customer interacting with the company, or the customer interacting potentially with our ecosystem partners, mutual partners, and then letting the best in practice supply chain techniques, make sure that we're bringing the highest level of service and support to that entire lifecycle. And when we bring data under management, manage it through its lifecycle and have the historical view at rest, and leverage that across every tier, that's when we get these high velocity, deep transparency, and connectivity between each of the constituents in the value chain, and that's what our platforms give them the ability to do. >> Not only your platform, you guys have been in business now for I think seven years or so, and you shifted from being in the minds of many and including your own strategy from being the premier data at rest company in terms of the a Hadoop platform to being one of the premier data in motion companies. Is that really where you're going? To be more of a completely streaming focus, solution provider in a multi-cloud environment? And I hear a lot of Kafka in your story now that it's like, oh yeah, that's right, Hortonworks is big on Kafka. Can you give us just a quick sense of how you're making that shift towards low latency real time streaming, big data, or small data for that matter, with embedded analytics and machine learning? >> So, we have evolved from certainly being the leader in global data platforms with all the work that we do collaboratively, and in through the community, to make Hadoop an enterprise viable data platform that has the ability to run mission critical workloads and apps at scale, ensuring that it has all the enterprise facilities from security and governance and management. But you're right, we have expanded our footprint aggressively. And we saw the opportunity to actually create more value for our customers by giving them the ability to not wait til they bring data under management to gain an insight, because in that case, they're happened to be reactive post event post transaction. We want to give them the ability to shift their business model to being interactive, pre-event, pre-conditioned. The way to do that we learned was to be able to bring the data under management from the point of origination, and that's what we used MiNiFi and NiFi for, and then HDF, to move it through its lifecycle, and your point, we have the intellect, we have the insight, and then we have the ability then to process the best in class outcome based on what we know the variables are we're trying to solve for as that's happening. >> And there's the word, the phrase asset which of course is a transactional data paradigm plan, I hear that all over your story now in streaming. So, what you're saying is it's a completely enterprise-grade streaming environment from n to n for the new era of edge computing. Would that be a fair way of-- >> It's very much so. And our model and strategy has always been bring the other best in class engines for what they do well for their particular dataset. A couple of examples of that, one, you brought up Kafka, another is Spark. And they do what they do really well. But what we do is make sure that they fit inside an overall data architecture that then embodies their access to a much broader central dataset that goes from point of origination to point of rest on a whole central architecture, and then benefit from our security, governance, and operations model, being able to manage those engines. So what we're trying to do is eliminate the silos for our customers, and having siloed datasets that just do particular functions. We give them the ability to have an enterprise modern data architecture, we manage the things that bring that forward for the enterprise to have the modern data driven business models by bringing the governance, the security, the operations management, ensure that those workflows go from beginning to end seamlessly. >> Do you, go ahead. >> So I was just going to ask about the customer concerns. So here you are, you've now given them this ability to make these real time changes, what's sort of next? What's on their mind now and what do you see as the future of what you want to deliver next? >> First and foremost we got to make sure we get this right, and we really bring this modern data architecture forward, and make sure that we truly have the governance correct, the security models correct. One pane of glass to manage this. And really enable that hybrid data architecture, and let them leverage the cloud tier where it's architecturally and financially pragmatic to do it, and give them the ability to leg into a cloud architecture without risk of either being locked in or misunderstanding where the lines of demarcation of workloads or datasets are, and not getting the economies or efficiencies they should. And we solved that with DataPlane. So we're working very hard with the community, with our ecosystem and strategic partners to make sure that we're enabling the ability to bring each type of data from any source and deploy it across any tier with a common security, governance, and management framework. So then what's next is now that we have this high velocity of data through its entire lifecycle on one common set of platforms, then we can start enabling the modern applications to function. And we can go look back into some of the legacy technologies that are very procedural based and are dependent on a transaction or an event happening before they can run their logic to get an outcome because that grinds the customer in post world activity. We want to make sure that we're bringing that kind of, for example, supply chain functionality, to the modern data architecture, so that we can put real time inventory allocation based on the patterns that our customers go in either how they're using the product, or frustrations they've had, or success they've had. And we know through artificial intelligence and machine learning that there's a high probability not only they will buy or use or expand their consumption of whatever that they have of our product or service, but it will probably to these other things as well if we do those things. >> Predict the logic as opposed to procedural, yes, AI. >> And very much so. And so it'll be bringing those what's next will be the modern applications on top of this that become very predictive and enabler versus very procedural post to that post transaction. We're little ways downstream. That's looking out. >> That's next year's conference. >> That's probably next year's conference. >> Well, Rob, thank you so much for coming on theCUBE, it's always a pleasure to have you. >> Thank you both for having us, and thank you for being here, and enjoy the summit. >> We're excited. >> Thank you. >> We'll do. >> I'm Rebecca Knight for Jim Kobielus. We will have more from DataWorks Summit just after this. (upbeat music)

>> Announcer: Live, from Las Vegas, it's theCUBE! Covering Informatica World 2018. Brought to you by Informatica. >> Hello everyone welcome back, this is theCUBE's exclusive coverage at Informatica World 2018 here live, in Las Vegas at The Venetian Ballroom. I'm John Furrier, your host of theCUBE, with Peter Burris, my co-host this week, Analyist at Wikibon, Chief Analyst at SiliconANGLE and theCUBE. Our next guest is Sanjeev Vohra, Group Technology Officer at Accenture, in charge of incubating new businesses, growing new businesses, handling the talent. Great to have you on thanks for spending the time coming on. >> Pleasure, it's my pleasure to be here. >> So we have a lot of Accenture interviews, go to thecube.net, type in Accenture, you'll see all the experts. And one of the things we love about talking with Accenture, is you guys are in the front lines of all the action. You have all the customer deployments, global system integrator, but you've got to be on top of the new technology, you've got really smart people, so thanks for spending the time. So I got to ask you, looking at the landscape, of the timing of Informatica's opportunity, you've got data, which is not a surprise for some people, but you've got GDPR happening on, this Friday, you've got cloud scale on the horizon, a lot of interesting things are going on right now around data and the impact of customers, which is now pretty much front and center. What're you guys doing with Informatica, what are some of the things that you guys are engaging with them on, and what's important to you? >> We have a very deep relationship with Informatica for many years and, we have many, many, joint clients in the market, and we are helping them sustain their businesses, and also grow their businesses future. Right? In future. And I think, I think there's a lot going on, there's a lot going on sustaining the core of the business, and improving it on a continuous basis, by using new technologies, and, you know, like today's keynote went on a little, talked about the new stuff and it's, there's a lot of things, actually, clients require, or our customers require for, just sustaining their core. But then I caught something in the middle, which is basically: how are you building your new business models, how are you disrupting the market your industry, what's new around that? And, in that piece, I think that's where, we are now starting working with Informatica to see what other pieces we need to bring together to the market, so we can generate, so we can help clients or customers to really leverage the power of technology. And I'll tell you, there are four areas of discussion priorities, that are, you know, you get a sense, and we get a deep dive depending on what you want to see. The first one is, I think the customers now have data warehouses, which are Data 2.0, as is what's told in the morning, so these are still 15 years old data warehouses, they are not in the new. So a lot of customers, and a lot of organizations, large organizations, including some organizations like ours, they're investing right now to make sure that they get to Data 3.0, which is what Anil was saying in the morning, which is around the new data supply chain, because without that, you cannot actually get real data analytics. Right? So you can't generate insight on analytics unless you actually work on your data's infrastructure layer below, so that's one area where we are working with them, that's where the cloud comes in, that's where the flexibility of cloud comes in. The second piece is around, around data compliance and governance because, guess what, there're regulations which are coming up now, which are towards data privacy and data protection. And the data infrastructures which were built 15 years back, actually do not handle that so effectively. >> In being polite, yeah. I mean, it wasn't built for it, they didn't have to think about it. >> Sanjeev: It was not built for that, exactly. So now, now, the point there is that, now there is a regulation coming in, one of them is GDPR, Global Data Protection Regulation, it impacts all the global companies who deal with your EU residents. And now they are looking at how they can address that regulation, and be compliant with that regulation. And we believe that's a great opportunity for them to actually invest. And see how, not only comply with regulation, but actually make this a benefit for them. And make the next leap towards building a next level of infrastructure for them, their data, right? >> And that is doing a lot of the data engineering, actually getting data right. >> And that's the third piece. So the first two are this: one is infrastructure, second is compliance, and the third reason, they're all interrelated finally, but I'm just saying, it depends on, from where do you want to begin your journey, right? And the third piece is around, I think you got it right, is about quality of data, but actually it is not quality, we call it data voracity, it's much beyond quality. We talk about more completeness, and also things like provenance, integrity, and security along with it, so if we, and it's very much business contextual element, because what's happening is, you may have heard the story is that, clients have invested in data lakes, for years now, it's been there for like, eight, nine years, data lake concepts, and everybody talks about it-- >> John: Throw everything into the lake. >> And everybody says throw everything into the lake, and then they become a data swamp. (John laughing) - That was last years theme. >> That was last years theme, and the reason is because, because it's not IT's failure, IT is actually pretty advanced, the technology is very advanced. If the business is not as involved as it should be, and is not able to trust the data, and that's where your point comes in, whether you have the right data, and trusted data with you. >> Though, well we had Toyota on earlier and they said, one of the customers said, we had this 2008 post crisis thing and then, they had all this stuff channeled, they had product in channel, and they had the data! They actually had the data, they didn't have access to it! So again, this is like the new data center, data first, get it right, and so with GDPR we're seeing people saying okay, we've got to get this right. So that's, investing engineering involved, governance, application integration, this is all, now, a new thing. How do you guys advise you clients? 'Cause this is super important and you guys are, again, on the front edge. As a CTO group, you got to look at the new tech and say, okay, that's baked, that's not baked, that's new, that's old, throw a container around it, you know. (laughing) How are you sorting through the tools, the platforms? 'Cause there's a lot of, there's a lot of stuff out there. >> Oh yes, absolutely, and there's a lot of stuff, and there's a lot of unproven things as well, in the market. So, the first and foremost thing is that, we should understand what the context in the market right now is. The first question is, mine is, is everybody ready for GDPR? The answer is no. (John laughs) Are they, have they started into the journey, have they started getting on the racetrack, right, on the road? >> Yes? Yeah? It depends on a majority of that organization, some people have just started building a small strategy around GDPR, some people have actually started doing assessments to understand how complex is this beast, and regulation, and some people have just moved further in the journey of doing assessment, but they're now putting up changes in their infrastructure to handle remediation, right? Things like, for example, consent management, thinks about things like dilation, like, it's going to be a very big deal to do, right? And so they are making advantageous changes to the infrastructure that they have, or the IT systems to manage it effectively. But I don't think there's any company which properly can claim that have got it right fully, from end-to-end, right? So I think that's happening. Now, how are we addressing? I think the first and foremost thing, first of all we need to assess the majority of the customers, or the organization. Like BHD, because we talk to them first and understand, we understand, right? Usually we have various ways of doing it, we can have a chit-chat, and meet the person responsible in that company, it could be a Chief Data Officer of a company, it could be a CIO of a company, it could Chief Operating Officer of a company, it could be a CSO of a company, depending on who has a baton in the sea of suites, to kind of handle this problem. >> So it's different per company, right, so every company has their own hierarchy or need, or entry point? >> Data companies have different entry points, but we are seeing more of the CSOs and CIOs playing a role in many of the large organizations, and our, you know our clientele is very large companies, as you know. But we see most of these players playing that role, and asking for help, and asking for having a meeting, and starting with that. In some cases, they have not invested initially, we talked to them, we assess them very quickly, very easy, quick as it's in, you know, probably in a couple of days or day, and tell them that, let's get into a, what we call is, assessment as step one, and that takes four to six weeks, or eight weeks, depending on the size of their application suite, and the organization. And we do it quite fast, I mean initially, we were also learning. If you were to have asked me this question 12 months back, we had an approach. We've changed that approach and evolved that approach now. We invested hugely in that approach itself, by using a lot of machine learning to do assessment itself. So we have now a concept called data discovery, another concept called knowledge graph. >> And that's software driven, both with, it's all machine learning or? >> Sanjeev: It's largely computer driven. But obviously human and computer work together, but it's not only human. A traditional approach would happen to do only with humans. >> John: Yeah, and that've been takin' a long time. >> And that has changed, that has changed with the new era, and technology advancement, that even for, things which are like assessment, could now be done by machines as well, machines are smart enough to do that work, so we are using that right now. But that's a step one, and after that, once we get there, we build a roadmap for them, we ensure that they're stakeholders are agreeing with the roadmap, they actually embrace the roadmap! (laughing) And once that's done, then we talk about remediation to their systems. >> So, you mention voracity, one of the, and you also mentioned, for example, the idea of the, because of GDPR, deletion, which is in itself a voracity thing, so you, it's also having a verifiable actions on data. So, the challenge that you face, I think, when you talk to large customers, John mentioned Toyota, is, the data's there, but sometimes it's not organized for new classes of problems, so, and that's an executive issue 'cause, a lot of executives don't think in terms of new problem, new data, new organization. You guys are speaking to the top executives, CSOs, CIOs often but, how are you encouraging your clients, your customers, to think differently, so that they become data-first? Which is, kind of a predicate for digital business transformation anyway. >> So I think it's a great question. I think it depends again on, who you're talking to in the organization. I have a very strong perspective, my personal view is that data is an intersection of business and technology, it is not a technology, it's not a business, right? It's an intersection of both, especially this topic, it has to be done in collaboration within business and technology. Very closely in terms of how, what is the, how you can drive metadata out of your data, how can you drive advantage out of your data? And, having said that, I think the important thing to note down is that: for every, when you talk about data voracity, the single comment I will make that it is very, very, very contextual to business. Data voracity is very, very contextual to the business that you're running. >> Well, but problems, right? Because, for example, going to Toyota, so, when the Toyota gentleman came on, and this is really important, >> Absolutely. >> the manufacturing people are doing a great job of using data, lean is very data-driven. The marketing people were doing a great job of using data, the sales people were making a great job of using data, the problem was, the problems that Toyota faced in 2008, when the credit crunch hit, were not limited. They were not manufacturing problems, or marketing problems, or sales problems, they were a wholistic set of problems. And he discovered, Toyota discovered, they needed to say, what's the problem, recast the problem, and what can we do to get the data necessary to answer some of these crucial questions that we have? >> So, I think you hit the nail, I can tell, I mean, I think you're spot on, and the one way we are doing right now, addressing that is through, what we call our liquid studios, >> John: I'm just going to-- >> Peter: I'm sorry what? >> Liquid studios. >> Peter: Liquid studios. >> We have this concept called liquid studios. >> John: Yeah, yeah. >> And actually, this concept we started, I don't know if you heard about this from Accenture before? we started this thing couple of years back-- >> John: Well take a minute to explain that, that's important, explain liquid studios. >> Okay, so liquid studios, so what, when we were thinking about these things where, we talked to multiple clients, they called us, exactly the point, they may be working in silence, and they may be doing a great job in their department, or their function, but they are talking across enterprise. As to how they can, if you are doing great work, can I use your work for my advantage, and vice versa, right, because it's all sharing data, even inside enterprise, forget outside enterprise, and you will be amazed to know how much sharing happens today, within enterprise, right? And you're smiling, right, so? So what we did was, we came to this concept, and the technologies are very new and very advanced, and many of the technologies we are not using beyond experimentation, we are still in the COE concept, well that's different than enterprise ready deployment. Like, if we talk about ERP today, that's not a COE, that's an enterprise ready deployment, in most of the companies, it's all there, like, you run your finance on ERPs right, most of the companies, big companies. So we felt that, technology's advancing, the business and technology IOs, they all have to still agree on a concept, and define a problem together. And that's where the studio comes in, so what we do is, it's actually a central facility, very innovative and creative space, it's unlike an office, it's very much like, new, new thing, it's like very, differently organized structure to generate creativity and good discussion. And we bring in core customers there, we have a workshop with them, we talk about the problem for one or two days, we use design thinking for that, a very effective way. Because one thing we've learned, the one thing that brings our table to agreement on a problem. (laughing) (John and Peter laugh) In a very nice manner, without confronting, in a very subtle manner. So we, through this timeframe, we get to a good problem situation, a good problem definition and then, the studio can actually help you do the POC itself. Because many times people say, well I understand the problem, I think I kind of get your solution, or what your proposing, my people also tell me something else, they have a different option to propose. Can we do it together? Can I get the confidence that, I don't want to go in enterprise ready deployment and put my money, unless I see some proof of pudding, but proof of pudding is not a power point. It's the actual working mark. >> Peter: It's not?! >> It's not! (all laughing) and that's where the studio comes in picture because, you wouldn't believe that we do these two days of workshop without any Powerpoint, like we aren't on a single slide. >> So it's creative, it's very agile, very? >> It's more white boarding, come and talk, it's more visitation, more visitation now, more human interaction, and that's where you open up everybody saying: what is your view, what is your view? We use a lot of post-it stickies to kind of get the-- >> I think the business angle's super important, I want to get your thoughts. 'Cause there's a lot of problems that can be solved once you identify them. But we're hearing terms like competitive advantage, 'cause when you solve some of these problems, these wholistic problems, that have a lot of interplay, where data's shared, or where there's internal, and or external with APIs and cloud-native, you start thinking about competitive advantages, being the data-first company, we've heard these terms. What does that mean to you guys? When you walk into an executive briefing, and they say look, you know, we've done all this work, we've done this engineering, here's where we're at, we need help, but ultimately we want to drive top-line results, be more competitive, really kind of move with the shift. This is a, this is more of a business discussion, what do you guys talk about when you have those conversations? >> I think we, so first of all, data was always a technical topic, do you agree? Like if you just go back, 10 years back, data was always a CIO discussion. >> Well, >> Unless you're in a regulated industry like financial services or, >> Or I guess I'd say this, that the, that the notion of getting data out of a system, or getting data into a system, was a technical discussion. But there was, you know, we've always used data, from market share growth, etc. But that was relatively simple, straight-forward data, and what you're talking about, I think, is, getting into considerably greater detail about how the business is really operating, how the business is really working. Am I right? >> You're right, considering data as an asset, in a discussion in terms of, how can you leverage it effectively, that's what I was saying and, so it is, it's definitely gone up one more level upstaged or into the discussion that is, into the companies and organizations. And what we're saying is, that's where the business comes in effectively and say that, helping them understand, and by the way, the reason I was making that comment is because, if you have ever seen people expending data 10 years back, it is very complex explanation. >> Schemas, this, that, and the other thing. >> You got it, yeah. And it's very hard for a business guy to understand that, like if I'm a supply action lead, I don't get it, it's too complex for me. So what we did, I'm just letting you know how we started the discussion. The first and foremost thing is, we tell them, we're going to solve the business problem, to your point, that's what we think, right? And, every company now-a-days, they want to lead in their industry, and the leadership position is to be more intelligent. >> Yeah, and it's got to hit the mark, I mean, we had Graeme Thompson on, who's the CIO, here at Informatica, and he was saying that if you go to a CFO and ask them hey where's the money, they'll go oh, it's over here, they get your stuff, they know where it's stored, at risk management, they say, where's they data? You mentioned asset, this is now becoming a conversation, where it's like, certainly GDPR is one shot across the bow that people are standing up, taking notice, it's happening now. This data as a asset is a very interesting concept. When I'm a customer of yours, say, and I say hey Sanjeer, I have a need, I got to move my organization to be data-first but, I got to do some more work. What's my journey? I know it's different per customer, depending on whether it's top-down, or bottom-up, we see that a lot but. How do you guys take them through the journey? Is it the workshop, as you mentioned, the assessment, take us through the journey of how you help customers, because I'm sure a lot of them are sittin' out there goin' now, they're going to be exposed with GDPR, saying wow, were we really setup for this? >> Yeah, so I think in the journey, it's a very good question that you asked. The journey can start depending on the real, the biggest pain they have, and the pains could be different on the majority of that particular organization, right? But I can tell you what client position we are having, in a very simplified manner, so that you understand the journey, but yes, when we engage with them, there's a process we follow, we have a discovery process, we have a studio process, together have a workshop, get into a POC, get into a large-scale deployment solution en route. That's a simple thing, that's more sequential in nature, but the condition is around four areas. The first and foremost area is, many companies actually don't have any particular data strategy. They have a very well articulated IT strategy, and when you go to a section of IT strategy, there's a data component in that, but that's all technology. About how do you load, how do you extract those things. It talks about data architectures, and talks about data integration, but it doesn't talk about data as a business, right? That's where it's not there, right? In some companies they do have, to your point, yes, some companies were always there in data, because of regulatory concerns and requirements, so they always had a data organization, a function, which thought of data as different from other industries. And those industries have more better strategy documents or, or they're more organized in that space. But, guess what, now companies are actually investing. They're actually asking for doing help in data strategies, that's one entry point which happens, which means, hey, I understand this, I understand governance is required, I understand privacy's required, and I understand this is required, I also understand that I need to move to new infrastructure, but I can't just make an investment in one or two areas, can you help my build my strategy and road map as to what should be my journey from now til next three years, right, how does it look like? How much money is required, how much investment is required, how do I save from something and invest here, help me save internal wealth, right? That's a new concept. Right, because I don't have so much that you're asking for, so help me gain some savings somewhere else. That's where cloud comes in. (laughs) So, that's one entry point, the second entry point is totally on, where the customers are very clear, they actually have thought through the process, in terms of where they want to go, they actually are asking, very specifically saying, I do have a problem in our infrastructure, help me move to cloud. Help me, that's a big decision right, help me move to cloud, right? But that's one, which I call is, new data supply chain, that's my language. Which means that-- >> John: I like that word actually. >> Yeah? I'm making your supply chain and my supply chain in business terms, if I have to explain business, it's different, technically it's different. Technology, I can explain all the things that you just mentioned, in business I explain that there are three Cs to a supply chain, capture it, curate it, consume it, and they so, oh I get it now, that's easy! >> Well, the data supply chain is interesting too, when you think about new data coming in, the system has to be reactive and handle new data, so you have to have this catalog thing. And that was something that we saw a lot of buzz here at the show, this enterprise catalog. What's your take on that, what's your assessment of the catalog, impact to customers, purpose at this point in time? >> I think it's very important, especially with the customers and large companies, who actually have data all over the place. I can share, as an example, we were talking to one of the customers who had 2600 applications, and they want to go for GDPR, we had a chat with them, and we said look, they were more comfortable saying, no, no, let's no use any machine. Because when you talk about machine, then you have to expose yourself a bit, right? And I said look, the machine is not going to be in my place, it's going to be in yours, your boundaries of firewall. But they were a little more concerned, they said let's go with a manual approach, let's do that, I said fair enough, it's your call, we can do that as well. But guess what? 2600 applications, you can't discover manually, it's just not possible. >> John: Yeah, you need help. A lot of data streaming and-- >> I guess I'm just letting you know it's very, I'm just answering your question. The data catalog is extremely important, if you really want to get a sense of where the data is residing, because data is not in one or two applications, it's all over the place. >> Well I'm impressed by the data catalog positioning, but then also, when you look at the Azure announcement they had, that Informatica had. You're essentially seeing hybrid cloud playing out as a real product. So that's an easy migration, of bringing in some of those BI tools, bringing some democratization into the data discovery. Rajeev, thanks for coming on theCUBE, really appreciate it, love the work you do, and I just want you to take a minute, just to end the segment out. Explain the work that you do, you have two roles, real quick, explain your two primary roles. You've got the, you incubate new stuff, which is hard to do, but, I'm an entrepreneur, I love the hard problems, but also you're doing talent. Take a minute to kind of explain, real quickly, those two roles, for, super important. >> well, the first one is basically that I, my role, I look at any ideas that are, that we can incubate as a business, and we can work within Accenture, different entities within Accenture to make sure that we go to clients in a much more quiescent manner, and see how we can have an impact to our top line. And that's a big thing, because our, we are a service as a business and, we have to be very innovative to come to know how do we increase our business. >> Any examples that you can share, of that stuff that you worked on? >> So, one is, right now, I'm spending a lot of my time in, on fueling our data business itself. We just recently launched our data business group, right? We have our market way in this position, is called applied intendance, which you may be aware, which includes data, analytics, advanced analytics, and then artificial intelligence, all put together, then we can solve these problems. >> And you guys got a zillion data scientists, I know that, you guys have been hiring really, really strong people. >> It's a very strong team. But on that, what I feel is that, the data is a critical foundation, really critical foundation for an intelligent enterprise. You can become and intelligent enterprise unless you have right data, to your point. And right data means curated data, in the set, in the fashion that can help you become, draw more insights from your enterprise. And that's possible if you invest in data strongly, and selection of data so strongly, but that's why we are fueling that, so I'm just letting you know that I'm spending most of my time right now to enhance our capability, you know, enhance our power in on that, and go to market with that. The second thing which I am investing right now, which is, there is a few more ideas, but one more, which could be very useful for you to know, is, while companies are moving to the new, they have to also, they have to rely on their people. Ultimately the companies are made of people. Like us, right? And if you can, if you are not retooling yourself, you cannot reimagine the future of your organization as well. >> You're talking about the peoples, their own skills, their job functions, okay-- >> So I'm working on a concept called workforce of the future right, how can 44 companies, large companies, how can they transform their talent, and their, even leadership as well, so that they are ready for the future and they can be more relevant. >> Yeah, and this is the argument we always see on theCUBE, oh, automation's going to take jobs away, well, I mean certainly automating repetitive tasks, no one wants to do those, (laughing) but the value is going to shift, that's where the opportunities are, is that how you see that future workforce? >> Absolutely, it's one of the complimentary, we have Paul Daugherty, whom you know, who's the Chief Technology Officer of Accenture Technology. Accenture, Accenture as a firm, he, he's a Chief Technology and Innovation Officer for Accenture He has recently written a book called Human + Machine, exactly talked about the same concept that, we actually all believe, very, very strongly that, the future is all about augmenting humans together. So there are tasks which machines should be doing, and there are tasks where humans should be doing, and there are tasks which both of them do collaboratively, and that's what we are trying to boast. >> Cloud world, we're doing it here in theCUBE, here at Informatica World. Rajeev, thanks so much for spending time-- >> Sajeev. (laughing) Sajeev, I mean, thanks for coming on. Sorry my bad, a little late in the day. But we're bringing it out here at Informatica World, this is theCUBE, I'm John Furrier with Peter Burris, here with Accenture inside theCUBE, here at Informatica World in Las Vegas. Be right back with more coverage, after this short break. Thank you. (bubbly music)