(upbeat music) >> Hello, everyone, welcome to theCUBE's presentation of the AWS Startup Showcase. We're in Season two, Episode three, and this is the topic of MarTech and the Emerging Cloud-Scale Customer Experiences, the ongoing coverage of AWS's ecosystem of large scale growth and new companies and growing companies. I'm your host, John Furrier. We're excited to have Tim Barnes, Global Director, General Manager of Advertiser and Marketing at AWS here doing the keynote cloud-scale customer experience. Tim, thanks for coming on. >> Oh, great to be here and thank you for having me. >> You've seen many cycles of innovation, certainly in the ad tech platform space around data, serving consumers and a lot of big, big scale advertisers over the years as the Web 1.0, 2.0, now 3.0 coming, cloud-scale, roll of data, all big conversations changing the game. We see things like cookies going away. What does this all mean? Silos, walled gardens, a lot of new things are impacting the applications and expectations of consumers, which is also impacting the folks trying to reach the consumers. And this is kind of creating a kind of a current situation, which is challenging, but also an opportunity. Can you share your perspective of what this current situation is, as the emerging MarTech landscape emerges? >> Yeah, sure, John, it's funny in this industry, the only constant has changed and it's an ever-changing industry and never more so than right now. I mean, we're seeing with whether it's the rise of privacy legislation or just breach of security of data or changes in how the top tech providers and browser controllers are changing their process for reaching customers. This is an inflection point in the history of both ad tech and MarTech. You hit the nail on the head with cookie deprecation, with Apple removing IDFA, changes to browsers, et cetera, we're at an interesting point. And by the way, we're also seeing an explosion of content sources and ability to reach customers that's unmatched in the history of advertising. So those two things are somewhat at odds. So whether we see the rise of connected television or digital out of home, you mentioned Web 3.0 and the opportunities that may present in metaverse, et cetera, it's an explosion of opportunity, but how do we continue to connect brands with customers and do so in a privacy compliant way? And that's really the big challenge we're facing. One of the things that I see is the rise of modeling or machine learning as a mechanism to help remove some of these barriers. If you think about the idea of one-to-one targeting, well, that's going to be less and less possible as we progress. So how am I still as a brand advertiser or as a targeted advertiser, how am I going to still reach the right audience with the right message in a world where I don't necessarily know who they are. And modeling is a really key way of achieving that goal and we're seeing that across a number of different angles. >> We've always talked about on the ad tech business for years, it's the behemoth of contextual and behavioral, those dynamics. And if you look at the content side of the business, you have now this new, massive source of new sources, blogging has been around for a long time, you got video, you got newsletters, you got all kinds of people, self-publishing, that's been around for a while, right? So you're seeing all these new sources. Trust is a big factor, but everyone wants to control their data. So this walled garden perpetuation of value, I got to control my data, but machine learning works best when you expose data, so this is kind of a paradox. Can you talk about the current challenge here and how to overcome it because you can't fight fashion, as they say, and we see people kind of going down this road as saying, data's a competitive advantage, but I got to figure out a way to keep it, own it, but also share it for the machine learning. What's your take on that? >> Yeah, I think first and foremost, if I may, I would just start with, it's super important to make that connection with the consumer in the first place. So you hit the nail on the head for advertisers and marketers today, the importance of gaining first party access to your customer and with permission and consent is paramount. And so just how you establish that connection point with trust and with very clear directive on how you're going to use the data has never been more important. So I would start there if I was a brand advertiser or a marketer, trying to figure out how I'm going to better connect with my consumers and get more first party data that I could leverage. So that's just building the scale of first party data to enable you to actually perform some of the types of approaches we'll discuss. The second thing I would say is that increasingly, the challenge exists with the exchange of the data itself. So if I'm a data control, if I own a set of first party data that I have consent with consumers to use, and I'm passing that data over to a third party, and that data is leaked, I'm still responsible for that data. Or if somebody wants to opt out of a communication and that opt out signal doesn't flow to the third party, I'm still liable, or at least from the consumer's perspective, I've provided a poor customer experience. And that's where we see the rise of the next generation, I call it of data clean rooms, the approaches that you're seeing, a number of customers take in terms of how they connect data without actually moving the data between two sources. And we're seeing that as certainly a mechanism by which you can preserve accessibility data, we call that federated data exchange or federated data clean rooms and I think you're seeing that from a number of different parties in the industry. >> That's awesome, I want to get into the data interoperability because we have a lot of startups presenting in this episode around that area, but why I got you here, you mentioned data clean room. Could you define for us, what is a federated data clean room, what is that about? >> Yeah, I would simply describe it as zero data movement in a privacy and secure environment. To be a little bit more explicit and detailed, it really is the idea that if I'm a party A and I want to exchange data with party B, how can I run a query for analytics or other purposes without actually moving data anywhere? Can I run a query that has accessibility to both parties, that has the security and the levels of aggregation that both parties agree to and then run the query and get those results sets back in a way that it actually facilitates business between the two parties. And we're seeing that expand with partners like Snowflake and InfoSum, even within Amazon itself, AWS, we have data sharing capabilities within Redshift and some of our other data-led capabilities. And we're just seeing explosion of demand and need for customers to be able to share data, but do it in a way where they still control the data and don't ever hand it over to a third party for execution. >> So if I understand this correctly, this is kind of an evolution to kind of take away the middleman, if you will, between parties that used to be historically the case, is that right? >> Yeah, I'd say this, the middleman still exists in many cases. If you think about joining two parties' data together, you still have the problem of the match key. How do I make sure that I get the broadest set of data to match up with the broadest set of data on the other side? So we have a number of partners that provide these types of services from LiveRamp, TransUnion, Experian, et cetera. So there's still a place for that so-called middleman in terms of helping to facilitate the transaction, but as a clean room itself, I think that term is becoming outdated in terms of a physical third party location, where you push data for analysis, that's controlled by a third party. >> Yeah, great clarification there. I want to get into this data interoperability because the benefits of AWS and cloud scales we've seen over the past decade and looking forward is, it's an API based economy. So APIs and microservices, cloud native stuff is going to be the key to integration. And so connecting people together is kind of what we're seeing as the trend. People are connecting their data, they're sharing code in open source. So there's an opportunity to connect the ecosystem of companies out there with their data. Can you share your view on this interoperability trend, why it's important and what's the impact to customers who want to go down this either automated or programmatic connection oriented way of connecting data. >> Never more important than it has been right now. I mean, if you think about the way we transact it and still too today do to a certain extent through cookie swaps and all sorts of crazy exchanges of data, those are going away at some point in the future; it could be a year from now, it could be later, but they're going away. And I think that that puts a great amount of pressure on the broad ecosystem of customers who transact for marketers, on behalf of marketers, both for advertising and marketing. And so data interoperability to me is how we think about providing that transactional layer between multiple parties so that they can continue to transact in a way that's meaningful and seamless, and frankly at lower cost and at greater scale than we've done in the past with less complexity. And so, we're seeing a number of changes in that regard, whether that's data sharing and data clean rooms or federated clean rooms, as we described earlier, whether that's the rise of next generation identity solutions, for example, the UID 2.0 Consortium, which is an effort to use hashed email addresses and other forms of identifiers to facilitate data exchange for the programmatic ecosystem. These are sort of evolutions based on this notion that the old world is going away, the new world is coming, and part of that is how do we connect data sources in a more seamless and frankly, efficient manner. >> It's almost interesting, it's almost flipped upside down, you had this walled garden mentality, I got to control my data, but now I have data interoperability. So you got to own and collect the data, but also share it. This is going to kind of change the paradigm around my identity platforms, attributions, audience, as audiences move around, and with cookies going away, this is going to require a new abstraction, a new way to do it. So you mentioned some of those standards. Is there a path in this evolution that changes it for the better? What's your view on this? What do you see happening? What's going to come out of this new wave? >> Yeah, my father was always fond of telling me, "The customer, my customers is my customer." And I like to put myself in the shoes of the Marc Pritchards of the world at Procter & Gamble and think, what do they want? And frankly, their requirements for data and for marketing have not changed over the last 20 years. It's, I want to reach the right customer at the right time, with the right message and I want to be able to measure it. In other words, summarizing, I want omnichannel execution with omnichannel measurement, and that's become increasingly difficult as you highlighted with the rise of the walled gardens and increasingly data living in silos. And so I think it's important that we, as an industry start to think about what's in the best interest of the one customer who brings virtually 100% of the dollars to this marketplace, which is the CMO and the CMO office. And how do we think about returning value to them in a way that is meaningful and actually drives its industry forward. And I think that's where the data operability piece becomes really important. How do we think about connecting the omnichannel channels of execution? How do we connect that with partners who run attribution offerings with machine learning or partners who provide augmentation or enrichment data such as third party data providers, or even connecting the buy side with the sell side in a more efficient manner? How do I make that connection between the CMO and the publisher in a more efficient and effective way? And these are all challenges facing us today. And I think at the foundational layer of that is how do we think about first of all, what data does the marketer have, what is the first party data? How do we help them ethically source and collect more of that data with proper consent? And then how do we help them join that data into a variety of data sources in a way that they can gain value from it. And that's where machine learning really comes into play. So whether that's the notion of audience expansion, whether that's looking for some sort of cohort analysis that helps with contextual advertising, whether that's the notion of a more of a modeled approach to attribution versus a one-to-one approach, all of those things I think are in play, as we think about returning value back to that customer of our customer. >> That's interesting, you broke down the customer needs in three areas; CMO office and staff, partners ISV software developers, and then third party services. Kind of all different needs, if you will, kind of tiered, kind of at the center of that's the user, the consumer who have the expectations. So it's interesting, you have the stakeholders, you laid out kind of those three areas as to customers, but the end user, the consumer, they have a preference, they kind of don't want to be locked into one thing. They want to move around, they want to download apps, they want to play on Reddit, they want to be on LinkedIn, they want to be all over the place, they don't want to get locked in. So you have now kind of this high velocity user behavior. How do you see that factoring in, because with cookies going away and kind of the convergence of offline-online, really becoming predominant, how do you know someone's paying attention to what and when attention and reputation. All these things seem complex. How do you make sense of it? >> Yeah, it's a great question. I think that the consumer as you said, finds a creepiness factor with a message that follows them around their various sources of engagement with content. So I think at first and foremost, there's the recognition by the brand that we need to be a little bit more thoughtful about how we interact with our customer and how we build that trust and that relationship with the customer. And that all starts with of course, opt-in process consent management center but it also includes how we communicate with them. What message are we actually putting in front of them? Is it meaningful, is it impactful? Does it drive value for the customer? I think we've seen a lot of studies, I won't recite them that state that most consumers do find value in targeted messaging, but I think they want it done correctly and there in lies the problem. So what does that mean by channel, especially when we lose the ability to look at that consumer interaction across those channels. And I think that's where we have to be a little bit more thoughtful with frankly, kind of going back to the beginning with contextual advertising, with advertising that perhaps has meaning, or has empathy with the consumer, perhaps resonates with the consumer in a different way than just a targeted message. And we're seeing that trend, we're seeing that trend both in television, connected television as those converge, but also as we see about connectivity with gaming and other sort of more nuanced channels. The other thing I would say is, I think there's a movement towards less interruptive advertising as well, which kind of removes a little bit of those barriers for the consumer and the brand to interact. And whether that be dynamic product placement, content optimization, or whether that be sponsorship type opportunities within digital. I think we're seeing an increased movement towards those types of executions, which I think will also provide value to both parties. >> Yeah, I think you nailed it there. I totally agree with you on the contextual targeting, I think that's a huge deal and that's proven over the years of providing benefit. People, they're trying to find what they're looking for, whether it's data to consume or a solution they want to buy. So I think that all kind of ties together. The question is these three stakeholders, the CMO office and staff you mentioned, and the software developers, apps, or walled gardens, and then like ad servers as they come together, have to have standards. And so, I think to me, I'm trying to squint through all the movement and the shifting plates that are going on in the industry and trying to figure out where are the dots connecting? And you've seen many cycles of innovation at the end of the day, it comes down to who can perform best for the end user, as well as the marketers and advertisers, so that balance. What's your view on this shift? It's going to land somewhere, it has to land in the right area, and the market's very efficient. I mean, this ad market's very efficient. >> Yeah, I mean, in some way, so from a standards perspective, I support and we interact extensively with the IB and other industry associations on privacy enhancing technologies and how we think about these next generations of connection points or identifiers to connect with consumers. But I'd say this, with respect to the CMO, and I mentioned the publisher earlier, I think over the last 10 years with the rise of programmatic, certainly we saw the power reside mostly with the CMO who was able to amass a large pool of cookies or purchase a large sort of cohort of customers with cookie based attributes and then execute against that. And so almost a blind fashion to the publisher, the publisher was sort of left to say, "Hey, here's an opportunity, do you want to buy it or not?" With no real reason why the marketer might be buying that customer? And I think that we're seeing a shift backwards towards the publisher and perhaps a healthy balance between the two. And so, I do believe that over time, that we're going to see publishers provide a lot more, what I might almost describe as mini walled gardens. So the ability, great publisher or a set of publishers to create a cohort of customers that can be targeted through programmatic or perhaps through programmatic guaranteed in a way that it's a balance between the two. And frankly thinking about that notion of federated data clean rooms, you can see an approach where publishers are able to share their first party data with a marketer's first party data, without either party feeling like they're giving up something or passing all their value over to the other. And I do believe we're going to see some significant technology changes over the next three to four years. That really rely on that interplay between the marketer and the publisher in a way that it helps both sides achieve their goals, and that is, increasing value back to the publisher in terms of higher CPMs, and of course, better reach and frequency controls for the marketer. >> I think you really brought up a big point there we can maybe follow up on, but I think this idea of publishers getting more control and power and value is an example of the market filling a void and the power log at the long tail, it's kind of a straight line. Then it's got the niche kind of communities, it's growing in the middle there, and I think the middle of the torso of that power law is the publishers because they have all the technology to measure the journeys and the click throughs and all this traffic going on their platform, but they just need to connect to someone else. >> Correct. >> That brings in the interoperability. So, as a publisher ourselves, we see that long tail getting really kind of fat in the middle where new brands are going to emerge, if they have audience. I mean, some podcasts have millions of users and some blogs are attracting massive audience, niche audiences that are growing. >> I would say, just look at the rise of what we might not have considered publishers in the past, but are certainly growing as publishers today. Customers like Instacart or Uber who are creating ad platforms or gaming, which of course has been an ad supported platform for some time, but is growing immensely. Retail as a platform, of course, amazon.com being one of the biggest retail platforms with advertising supported models, but we're seeing that growth across the board for retail customers. And I think that again, there's never been more opportunities to reach customers. We just have to do it the right way, in the way that it's not offensive to customers, not creepy, if you want to call it that, and also maximizes value for both parties and that be both the buy and the sell side. >> Yeah, everyone's a publisher and everyone's a media company. Everyone has their own news network, everyone has their own retail, it's a completely new world. Tim, thanks for coming on and sharing your perspective and insights on this key note, Tim Barnes, Global Director, General Manager of Advertiser and Market at AWS here with the Episode three of Season two of the AWS Startup Showcase. I'm John Furrier, thanks for watching. (upbeat music)

(upbeat music) >> Oracle is in the midst of an acceleration of its product cycles. It really has pushed new capabilities across its database, the database platforms, and of course the cloud in an effort to really maintain its position as the gold standard for cloud database. We've reported pretty extensively on Exadata, most recently the X9M that increased database IOPS and throughput. Organizations running mission critical OLTP, analytics and mix workloads tell us that they've seen meaningfully improved performance and lower costs, which you expect in a technology cycle. I often say if Oracle calls you out by name it's a compliment and it means you've succeeded. So just a couple of weeks ago, Oracle turned up the heat on MongoDB with a Mongo compatible API, in an effort to persuade developers to run applications in a autonomous database and on OCI, Oracle cloud infrastructure. There was a big emphasis by Oracle on acid compliance transactions and automatic scaling as well as access to multiple data types. This caught my attention because in the early days of no SQL, there was a lot of chatter from folks about not needing acid capability in the database anymore. Funny how that comes around. And anyway, you see Oracle investing, they spend money in R&D We've always said that`, they're protecting their moat. Now in social I've seen some criticisms like Oracle still is not adding enough new logos, and Oracle of course will dispute that and give you some examples. But to me what's most impressive is the big name customers that Oracle gets to talk in public. Deutsche Bank, Telephonic, Experian, FedEx, I mean dozens and dozens and dozens. I work with a lot of companies and the quality of the customers Oracle puts in front of analysts like myself is very very high. At the top of the list I would say. And they're big spending customers. And as we said many times when it comes to mission critical workloads, Oracle is the king. And one of the executives behind the success is a longtime Cube alum, Juan Loaiza who's executive vice president of mission critical technologies at Oracle. And we've invited him back on today to talk about some news and Oracle's latest developments and database, Juan welcome back to the show and thanks for coming on today and talking about today's announcement. >> I'm very happy to be here today with you. >> Okay, so what are you announcing and how does this help organizations particularly with those existing Exadata cloud at customer installations? >> Yeah, the big thing we're announcing is our very successful cloud at customer platform. We're extending the capabilities of our autonomous database running on it. And specifically we're allowing much smaller configurations so customers can start small and grow with our autonomous database on our cloud customer platform. >> So let's get into granularity a little bit and double click on this. Can you go over how customers, carve up VM clusters for different workloads? What's the tangible benefit to them? >> Yeah, so it's pretty straightforward. We deploy our Cloud@Customer system anywhere the customer wants it, let's say in their data center. And then through our cloud APIs and GUIs they can carve up into pieces into basically VMs. They can say, Hey, I want a VM with eight CPUs to do this, I want a VM with 20 CPUs to that, I want a 500 CPUVM to do something else. And that's what we call a VM cluster because in Cloud@Customer, it is a highly available environment. So you don't just get one VM, you get a cluster of highly available VMs. So you carve it up. You hand it out to different aspects of a company. You might have development on one, testing on another one, some production sales on one VM, marketing on a different VM. And then you run your databases in there and that's kind of how it works and it's all done completely through our GUI and it's very, very simple 'cause they use it the same cloud APIs and GUIs that we use in the public cloud. It is the same APIs and GUIs that we use in the public cloud. >> Yeah, I was going to say sounds like cloud. So what about prerequisites? What do customers have to do to take advantage of the new capabilities? Can they run it on an Exadata cloud a customer that they installed a couple years ago? Do they have to upgrade the hardware? What migration pain is involved? >> Yeah, there's no pain, so it's just, (coughs) excuse me. I can take their existing system, they get our free software update and they can just deploy autonomous database as a VM in their existing Exadata cloud system. >> Oh nice okay what's the bottom line dollars? Our audience are always interested in cutting costs. It's one of the reasons they're moving to the cloud for example. So how does autonomous database on VM clusters, on Exadata Cloud at Customer? How does it help cut their cost? >> Well, it's pretty straightforward. So previous to this a customer would have to have dedicated a system to either autonomous database or to non autonomous data. So you have to choose one together. So on a system by system basis, you chose I want this thing autonomous, or I don't want it autonomous. Now you carve in the VMs and say for this VM I want that autonomous for that VM I want to run a regular database managed database on there. So lets customers now start small with any size they want. They could start with two CPUs and run an autonomous database and that's all they pay for is the two CPUs that they use. >> Let's talk a little about traction. I mean, I remember we covered the original Exadata announcement quite a long time ago and it's obviously evolved and taken many forms. Look, it's hard to argue that it hasn't been a big success. It has for Oracle and your target customers. Does this announcement make Exadata cloud a customer more attractive for smaller companies. In other words, does it expand the team for ADB? And if so, how? >> Yeah, absolutely. I mean our Exadata cloud platform is extremely successful. We have thousands of deployments, we have on our data platform we have about almost 90% of the global fortune 100 and thousands of smaller customers. In the cloud we have now up to 40% of the global 100 a hundred biggest companies in the world running on that. So it's been extremely successful platform and cloud a customer is super key. A lot of customers can't move their data to the public cloud. So we bring the public cloud to them with our cloud customer offering. And so that's the big customer is the fortune hundred but we have thousands of smaller customers also. And the nice thing about this offering is we can start with literally two CPUs. So we can be a very small customer and still run our autonomous data based on our cloud customer platform. >> Well, everybody cares about security and governance. I mean, especially the big guys, but the little guys that in many ways as well they want the capabilities of the large companies but they can't necessarily afford it. So I want to talk about security in particular governance and it's especially important for mission-critical apps. So how does this all change the security in governance paradigm? What do customers need to know there? >> Yeah, so the beauty of autonomous database which is the thing that we're talking about today is Oracle deals with all the security. So the OS, the hardware, firmware, VMs, the database itself all the interfaces to the VM, to the database all that is it's all done by Oracle. So, which is incredibly important because there's a constant stream of security alerts that are coming out and it's very difficult for customers to keep up with this stuff. I mean, it's hard for us and we have thousands of engineers. And so we take that whole burden away from customers. And you just don't have to think about it, we deal with it. So once you deploy an autonomous database it is always secure because anytime a security alert comes out, we will apply that and we do it in an online fashion also. So it's really, particularly for smaller customers it's even harder because to keep up with all the security that you you need a giant team of security experts and even the biggest customers struggle with that and a small customer's going to really struggle. There's just two, you have to look at the entire stack, all the different components switches, firmware, OS, VMs, database, everything. It's just very difficult to keep up. So we do it all and for small cut, they just can't do it. So really they really need to partner with a company like Oracle that has thousands of engineers that can keep up with this stuff. >> It's true what you say, even large customers this CSOs will tell you that lack of talent, lack of skill sets. They just don't have enough people and so even the big guys can't keep up. Okay, I want you to pitch me as though I'm a developer, which I'm not, but we got a lot of developers in our community. We'll be Cube con next month in Valencia, sell me on why a developer should lean into ADB on Exadata cloud as a customer? >> Yeah, it's very straightforward. So Oracle has the most advanced database in the industry and that's widely recognized by database analysts and experts in the field. Traditionally, it's been hard for a developer to use it because it's been hard to manage. It's been hard to set up, install, configure, patch, back up all that kind of stuff. Autonomous database does it all for you. So as a developer, you can just go into our console, click on creating a database. We ask you four questions, how big, how many CPUs how much storage and say, give your password. And within minutes you have a database. And at that point you can go crazy and just develop. And you don't have to worry about managing the database, patching the database, maintaining the security and the database backing up to all that stuff. You can instantly scale it. You can say, Hey, I want to grow it, you just click a button, take, grow it to much any size you want and you get all the mission critical capabilities. So it works for tiny databases but it is a stock exchange quality in terms of performance, availability, security it's a rock solid database that's super trivial. So what used to be a very complex thing is now completely trivial for a developer. So they get the best of both worlds, they get everything on the database side and it it's trivial for them to use. >> Wow, if you're doing all that stuff for 'em are they going to do on their weekends? Code? (chuckles) >> They should be developing their application and add value to their company that's kind of what they should focus on. And they can be looking at all sorts of new technologies like JSON and the database machine learning in the database graph in the database. So you can build very sophisticated applications because you don't have to worry about the database anymore. >> All right, let's talk about the competition. So it's always a topic I like to bring up with you. From a competitive perspective how is this latest and instantiation of Exadata cloud a customer X9M how's this different from running an AWS database service for instance on outpost, or let's say I want to run SQL server on Azure Stack or whatever Microsoft's calling it these days. Give us the competitive angle here. >> Yeah, there kind of is no real competition. So both Amazon and Microsoft have an at customer solution but they're very primitive. I mean, just to give you an example like Amazon doesn't run any of their premier database offerings at customers. So whether it's Aurora Redshift, doesn't run just plane does not run. It's not that it runs badly or it's got limited, just does not run. They can't run Oracle RDS on premise and same thing with Microsoft. They can't run Azure SQL, which is their premier database on their act customer platform. So that kind of tells you how limited that platform is when even their own premier offerings doesn't run on it. In contrast, we're running Exadata with our premier autonomous database. So it's our premier platform that's in use today by most of the biggest, banks, telecom to retailers et cetera in the world, thousands of smaller customers. So it's super mission critical, super proven with our premier cloud database, which is autonomous theory. So it couldn't be more black and white, this is a case where it's there really is no competition in the cloud of customer space on the database side. >> Okay, but let me follow up on that, Juan, if I may, so, okay. So it took you guys a while to get to the cloud, it's taken them a while to figure it on-prem. I mean, aren't they going to eventually sort of get there? What gives you confidence that you'll be able to to keep ahead? >> Well, there's two things, right? One is we've been doing this for a long time. I mean, that's what Oracle initially started as an on-prem and our Exadata platform has been available for over a decade. And we have a ton of experience on this. We run the biggest banks in the world already, it's not some hope for the future. This is what runs today. And our focus has always been a combination of cloud and on-prem their heart's not really in the on-prem stuff they really like. Amazon's really a public cloud only vendor and you can see from the result, it's not you can say, they can say whatever they want but you can see the results. Their outpost platform has been available for several years now and it still doesn't even run their own products. So you can kind of see how hard they're trying and how much they really care about this market. >> All right, boil it down if you just had a few things that you'd tell someone about why they should run ADB on Exadata cloud at customer, what would you say? >> It's pretty simple, which is it's the world's most sophisticated database made completely simple, that's it? So you get a stock exchange level database, you can start really small and grow and it's completely trivial to run because Oracle is automated everything within our autonomous data we use machine learning and a lot of automation to automate everything around the database. So it's kind of the best of both worlds. The best possible database starts as small as you want and is the simplest database in the world. >> So I probably should have asked you this while I was pushing the competitive question but this may be my last question, I promise. It's the age old debate It rages on, you got specialized databases kind of a right tool for the right job approach. That's clearly where Amazon is headed or what Oracle refers to is converge database. Oracle says its approach is more complete and "simpler." Take us through your thinking on this and the latest positioning so the audience can understand it a bit better. >> Yeah, so apps aren't what they used to business apps, data driven apps aren't what they used to be. They used to be kind of green screens where you just entered data. Now everyone's a very sophisticated app, they want to be have location, they want to have maps, they want to have graph in there. They want to have machine learning, they want machine learning built into the app. So they want JSON they want text, they want text search. So all these capabilities are what a modern app has to support. And so what Oracle's done is we provided a single solution that provides everything you need to build a modern app and it's all integrated together. It's all transactional. You have analytics built into the same thing. You have reporting built into the same thing. So it has everything you need to build a modern app. In contrast, what most of our competitors do is they give you these little solutions, say, okay here you do machine learning over here, you do analytics over there, you do JSON over here, you do spatial over here you do graph over there. And then it's left a developer to put an app together from all these pieces. So it's like getting the pieces of a card and having to assemble it yourself and then maintain it for the rest of your life, which is the even harder part. So one part upgrades, you got to test that. So of other piece upgrade or changes, you got to test that, you got to deal with all the security problems of all these different systems. You have to convert the data, you have to move the data back and forth it's extraordinarily complicated. Our converge database, the data sits in one place and all the algorithms come to the data. It's very simple, it is dramatically simpler. And then autonomous database is what makes managing it trivial. You don't really have to manage anything more because Oracle's automated the whole thing. >> So, Juan, we got a pretty good Cadence going here. I mean I really appreciate you coming on and giving us these little video exclusives. You can tell by again, that Cadence how frequently you guys are making new announcements. So that's great, congrats on yet another announcement. Thanks for coming back in the program appreciate it. >> Yeah, of course we invest heavily in data management. That's our core and we will continue to do that. I mean, we're investing billions of dollars a year and we intend to stay the leaders in this market. >> Great stuff and thank you for watching the Cube, your leader in enterprise tech coverage, this is Dave Vellante we'll see you next time.

(upbeat music) >> Hi, I'm Joe Rodriguez, Managing Director of Financial Services at Cloudera. Welcome to the Fight Fraud with Data session. At Cloudera we believe that fighting fraud begins with data. So financial services is Cloudera's largest industry vertical. We have approximately 425 global financial services customers, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks. Four out of the five top stock exchanges, eight out of the top 10 wealth management firms and all four of the top credit card networks. So as you can see, most financial services institutions utilize Cloudera for data analytics and machine learning. We also have over 20 central banks and a dozen or so financial regulators. So it's an incredible footprint which gives Cloudera lots of insight into the many innovations that our customers are coming up with. Criminals can steal thousands of dollars before a fraudulent transaction is detected. So the cost to purchase your account data is well worth the price to fraudsters. According to Experian, credit and a debit card account information sells on the dark web for a mere $5 with the CVV number and up to $110 if it comes with all the bank information, including your name, social security number, date of birth, complete account numbers, and other personal data. Our customers have several key data and analytics challenges when it comes to fighting financial crime. The volume of data that they need to deal with is huge and growing exponentially. All this data needs to be evaluated in real time. There are new sources of streaming data that need to be integrated with existing legacy data sources. This includes biometrics data and enhanced authentication video surveillance, call center data, and of course all that needs to be integrated with existing legacy data sources. There is an analytics Arms Race between the banks and the criminals, and the criminal networks never stop innovating. They also have to deal with disjointed security and governance. Security and governance policies are often set per data source or application requiring redundant work across workloads. And they have to deal with siloed environments. The specialized nature of platforms and people results in disparate data sources and data management processes. This duplicates efforts and divides the business risk and crime teams, limiting collaboration opportunities between them. CDP enhances financial crime solutions to be holistic by eliminating data gaps between siloed solutions, with an enterprise data approach, advanced data analytics and machine learning. By deploying an enterprise wide data platform, you reduce siloed divisions between business risk and crime teams and enable better collaboration through industrialized machine learning, you tighten up the loop between detection and new fraud patterns. Cloudera provides the data platform on which a best of breed applications can run and leverage integrated machine learning. Cloudera stands rather than replaces your existing fraud modeling applications. So Oracle, SAS, Actimize, to name a few, integrate with an enterprise data hub to scale the data, increase speed and flexibility and improve efficacy of your entire fraud system. It also centralizes the fraud workload on data that can be used for other use cases in applications like Enhanced KYC and Customer 360 for example. I just wanted to highlight a couple of our partners in financial crime prevention, Simudyne and Quantexa. So Simudyne provides fraud simulation using agent-based modeling machine learning techniques to generate synthetic transaction data. This data simulates potential fraud scenarios in a cost-effective GDPR-compliant virtual environment to significantly improve financial crime detection systems. Simudyne identifies future fraud topologies for millions of simulations that can be used to dynamically train new machine learning algorithms for enhanced identification. And Quantexa connects the dots within your data using dynamic entity resolution, and advanced network analytics to create context around your customers. This enables you to see the bigger picture and automatically assesses potential criminal behavior. Now let's go over some of our customers and how they're using Cloudera. First, we'll talk about United Overseas Bank or UOB. UOB is a leading full service bank in Asia with a network of more than 500 offices in 19 countries and territories, in Asia Pacific, Western Europe and North America. UOB built a modern data platform on Cloudera that gives it the flexibility and speed to develop new AI and machine learning solutions and to create a data-driven enterprise. UOB set up it's big data analytics center in 2017. It was Singapore's first centralized big data unit within a bank to deepen the bank's data analytic capabilities and to use data insights to enhance the bank's performance. Essential to this work was implementing a platform that could cost efficiently bring together data from dozens of separate systems and incorporate a range of unstructured data, including voice and text. Using Cloudera CDP and machine learning, UOB gained a richer understanding of its customer preferences to help make their banking experience simpler, safer, and more reliable. Working with Cloudera, UOB has a big data platform that gives business staff and data scientists, faster access to relevant and quality data for self-service analytics, machine learning and emerging artificial intelligence solutions. With new self-service analytics and machine learning driven insights, UOB has realized improvements in digital banking, asset management, compliance, AML, and more. Advanced AML detection capabilities, help analysts detect suspicious transactions either based on hidden relationships of shell companies and high risk individuals with Cloudera and machine learning technologies, UOB was able to enhance AML detection and reduce the time to identify new links from months to three weeks. Next, let's speak about MasterCard. So MasterCard's principle business is to process payments between banks and merchants and the credit issuing banks and credit unions of the purchasers who use the MasterCard brand debit and credit cards to make purchases. MasterCard chose Cloudera Enterprise for fraud detection and to optimize their DW infrastructure, delivering deep insights and best practices and big data security and compliance. Next, let's speak about Bank Rakyat in Indonesia or BRI. BRI is one of the largest and oldest banks in Indonesia and engages in the provision of general banking services. It's headquartered in Jakarta, Indonesia. BRI is well-known for its focus on microfinancing initiatives and serves over 75 million customers through its more than 11,000 offices and rural service outposts. BRI required better insight to understand customer activity and identify fraudulent transactions. The bank needed a solid foundation that allowed it to leverage the power of advanced analytics, artificial intelligence, and machine learning to gain better understanding of customers and the market. BRI used Cloudera Enterprise data platform to build an agile and reliable, predictive augmented intelligence solution to enhance its credit scoring system. And to address the rising concern around data security from regulators and customers, BRI developed a real-time fraud detection service powered by Cloudera and Kafka, BRI's data scientists developed a machine learning model for fraud detection by creating a behavioral scoring model based on customer savings, loan transactions, deposits, payroll and other financial real-time data. This led to improvements in its fraud detection and credit scoring capabilities, as well as the development of a new digital microfinancing product. With the enablement of real-time fraud detection, BRI was able to reduce the rate of fraud by 40%. It improved relationship manager productivity by two and a half fold. It improved the credit scoring system to cut down on micro-financing loan processing times from two weeks to two days to now two minutes. So fraud prevention is a good area to start with data focus if you haven't already. It offers a quick return on investment and it's a focused area that's not too entrenched across the company. To learn more about fraud prevention, go to www.cloudera.com, and you should schedule a meeting with Cloudera to learn even more. And with that, thank you for listening and thank you for your time. (upbeat music)

>>Uh, hi, I'm Joe Rodriguez, managing director of financial services at Cloudera. Uh, welcome to the fight fraud with a data session, uh, at Cloudera, we believe that fighting fraud with, uh, uh, begins with data. Um, so financial services is Cloudera's largest industry vertical. We have approximately 425 global financial services customers, uh, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks, uh, four out of the five top, uh, stock exchanges, uh, eight out of the top 10 wealth management firms and all four of the top credit card networks. So as you can see most financial services institutions, uh, utilize Cloudera for data analytics and machine learning, uh, we also have over 20 central banks and a dozen or so financial regulators. So it's an incredible footprint which gives Cloudera lots of insight into the many innovations, uh, that our customers are coming up with. Uh, criminals can steal thousands of dollars before a fraudulent transaction is detected. So the cost of, uh, to purchase a, your account data is well worth the price to fraudsters. Uh, according to Experian credit and debit card account information sells on the dark web for a mere $5 with the CVV number and up to $110. If it comes with all the bank information, including your name, social security number, date of birth, uh, complete account numbers and, and other personal data. >>Um, our customers have several key data and analytics challenges when it comes to fighting financial crime. The volume of data that they need to deal with is, is huge and growing exponentially. Uh, all this data needs to be evaluated in real time. Uh, there is, uh, there are new sources of, of streaming data that need to be integrated with existing, uh, legacy data sources. This includes, um, biometrics data and enhanced, uh, authentication, uh, video surveillance call center data. And of course all that needs to be integrated with existing legacy data sources. Um, there is an analytics arms race between the banks and the criminals and the criminal networks never stop innovating. They also we'll have to deal with, uh, disjointed security and governance, security and governance policies are often set per data source, uh, or application requiring redundant work, work across workloads. And, and they have to deal with siloed environments, um, the specialized nature of platforms and people results in disparate data sources and data management processes, uh, this duplicates efforts and, uh, divides the, the business risk and crime teams, limiting collaboration opportunities between CDP enhances financial crime solutions, uh, to be holistic by eliminating data gaps between siloed solutions with, uh, an enterprise data approach, uh, advanced, uh, data analytics and machine learning, uh, by deploying an enterprise wide data platform, you reduce siloed divisions between business risk and crime teams and enable better collaboration through industrialized machine learning. >>Uh, you tighten up the loop between, uh, detection and new fraud patterns. Cloudera provides the data platform on which a best of breed applications can run and leverage integrated machine learning cloud Derrick stands rather than replaces your existing fraud modeling applications. So Oracle SAS Actimize to, to name a few, uh, integrate with an enterprise data hub to scale the data increased speed and flexibility and improve efficacy of your entire fraud system. It also centralizes the fraud workload on data that can be used for other use cases in applications like enhanced KYC and a customer 360 4 example. >>I just, I wanted to highlight a couple of our partners in financial crime prevention, uh, semi dine, and Quintex, uh, uh, so send me nine provides fraud simulation using agent-based modeling, uh, machine learning techniques, uh, to generate synthetic transaction data. This data simulates potential fraud scenarios in a cost-effective, uh, GDPR compliant, virtual environment, significantly improved financial crime detection systems, semi dine identifies future fraud topologies, uh, from millions of, of simulations that can be used to dynamically train, uh, new machine learning algorithms for enhanced fraud identification and context, um, uh, connects the dots within your data, using dynamic entity resolution, and advanced network analytics to create context around your customers. Um, this enables you to see the bigger picture and automatically assesses potential criminal beads behavior. >>Now let's go some of our, uh, customers, uh, and how they're using cloud caldera. Uh, first we'll talk about, uh, United overseas bank, or you will be, um, you'll be, is a leading full service bank in, uh, in Asia. It, uh, with, uh, a network of more than 500 offices in, in 19 countries and territories in Asia, Pacific, Western Europe and north America UA, um, UOB built a modern data platform on Cloudera that gives it the flexibility and speed to develop new AI and machine learning solutions and to create a data-driven enterprise. Um, you'll be set up, uh, set up it's big data analytics center in 2017. Uh, it was Singapore's first centralized big data unit, uh, within a bank to deepen the bank's data analytic capabilities and to use data insights to enhance, uh, the banks, uh, uh, performance essential to this work was implementing a platform that could cost efficiently, bring together data from dozens of separate systems and incorporate a range of unstructured data, including, uh, voice and text, um, using Cloudera CDP and machine learning. >>UOB gained a richer understanding of its customer preferences, uh, to help make their, their banking experience simpler, safer, and more reliable. Working with Cloudera UOB has a big data platform that gives business staff and data scientists faster access to relevant and quality data for, for self-service analytics, machine learning and, uh, emerging artificial intelligence solutions. Um, with new self-service analytics and machine learning driven insights, you'll be, uh, has realized improvements in, in digital banking, asset management, compliance, AML, and more, uh, advanced AML detection capabilities, help analysts detect suspicious transactions either based on hidden relationships of shell companies and, uh, high risk individuals, uh, with, uh, Cloudera and machine learning, uh, technologies. You you'll be, uh, was able to enhance AML detection and reduce the time to identify new links from months 2, 3, 3 weeks. >>Excellent mass let's speak about MasterCard. So MasterCard's principle businesses to process payments between banks and merchants and the credit issuing banks and credit unions of the purchasers who use the MasterCard brand debit and credit cards to make purchases MasterCard chose Cloudera enterprise for fraud detection, and to optimize their DW infrastructure, delivering deepens insights and best practices in big data security and compliance. Uh, next let's speak about, uh, bank Rakka yet, uh, in Indonesia or Bri. Um, it, VRI is one of the largest and oldest banks in Indonesia and engages in the provision of general banking services. Uh, it's headquartered in Jakarta Indonesia. Uh, Bri is well known for its focus on financing initiatives and serves over 75 million customers through it's more than 11,000 offices and rural service outposts. Uh, Bri required better insight to understand customer activity and identify fraudulent transactions. Uh, the bank needed a solid foundation that allowed it to leverage the power of advanced analytics, artificial intelligence, and machine learning to gain better understanding of customers and the market. >>Uh, Bri used, uh, Cloudera enterprise data platform to build an agile and reliable, predictive augmented intelligence solution, uh, to enhance its credit scoring system and to address the rising concern around data security from regulators, uh, and customers, uh, Bri developed a real-time fraud detection service, uh, powered by Cloudera and Kafka. Uh, Bri's data scientists developed a machine learning model for fraud detection by creating a behavioral scoring model based on customer savings, uh, loan transactions, deposits, payroll and other financial, um, uh, real-time time data. Uh, this led to improvements in its fraud detection and credit scoring capabilities, as well as the development of a, of a new digital microfinancing product, uh, with the enablement of real-time fraud detection, VRI was able to reduce the rate of fraud by 40%. Uh, it improved, uh, relationship manager productivity by two and a half fold. Uh, it improved the credit score scoring system to cut down on micro-financing loan processing times from two weeks to two days to now two minutes. So fraud prevention is a good area to start with a data focus. If you haven't already, it offers a quick return on investment, uh, and it's a focused area. That's not too entrenched across the company, uh, to learn more about fraud prevention, uh, go to kroger.com and to schedule, and you should schedule a meeting with Cloudera, uh, to learn even more. Uh, and with that, thank you for listening and thank you for your time. >>Welcome to the customer. Obsession begins with data session. Uh, thank you for, for attending. Um, at Cloudera, we believe that a custom session begins with, uh, with, with data, um, and, uh, you know, financial services is Cloudera is largest industry vertical. We have approximately 425 global financial services customers, uh, which consists of 82 out of a hundred of the largest global banks of which we have 27 that are globally systemic banks, uh, four out of the five top stock exchanges, eight out of the 10 top wealth management firms and all four of the top credit card networks. Uh, so as you can see most financial services institutions utilize Cloudera for data analytics and machine learning. Uh, we also have over 20 central banks and it doesn't or so financial regulators. So it's an incredible footprint, which glimpse Cloudera, lots of insight into the many innovations that our customers are coming up with. >>Customers have grown more independent and demanding. Uh, they want the ability to perform many functions on their own and, uh, be able to do it. Uh, he do them on their mobile devices, uh, in a recent Accenture study, more than 50% of customers, uh, are focused on, uh, improving their customer experience through more personalized offers and advice. The study found that 75% of people are actually willing to share their data for better personalized offers and more efficient and intuitive services to get it better, better understanding of your customers, use all the data available to develop a complete view of your customer and, uh, and better serve them. Uh, this also breaks down, uh, costly silos, uh, shares data in, in accordance with privacy laws and assists with regulatory advice. It's so different organizations are going to be at different points in their data analytics and AI journey. >>Uh, there are several degrees of streaming and batch data, both structured and unstructured. Uh, you need a platform that can handle both, uh, with common, with a common governance layer, um, near real time. And, uh, real-time sources help make data more relevant. So if you look at this graphic, looking at it from left to right, uh, normal streaming and batch data comes from core banking and, uh, and lending operations data in pretty much a structured format as financial institutions start to evolve. Uh, they start to ingest near real-time streaming data that comes not only from customers, but also from, from newsfeeds for example, and they start to capture more behavioral data that they can use to evolve their models, uh, and customer experience. Uh, ultimately they start to ingest more real time streaming data, not only, um, standard, uh, sources like market and transaction data, but also alternative sources such as social media and connected sources, such as wearable devices, uh, giving them more, more data, better data, uh, to extract intelligence and drive personalized actions based on data in real time at the right time, um, and use machine learning and AI, uh, to drive anomaly detection and protect and predict, uh, present potential outcomes. >>So this is another way to look at it. Um, this slide shows the progression of the big data journey as it relates to a customer experience example, um, the dark blue represents, um, visibility or understanding your customer. So we have a data warehouse and are starting to develop some analytics, uh, to know your customer and start to provide a better customer 360 experience. Uh, the medium blue area, uh, is a customer centric or where we learn, uh, the customer's behavior. Uh, at this point we're improving our analytics, uh, gathering more customer centric information to perform, uh, some more exploratory, uh, data sciences. And we can start to do things like cross sell or upsell based on the customer's behavior, which should improve, uh, customer retention. The light blue area is, uh, is proactive customer inter interactions, or where we now have the ability, uh, to predict customers needs and wants and improve our interaction with the customer, uh, using applied machine learning and, and AI, uh, the Cloudera data platform, um, you know, business use cases require enabling, uh, the end-to-end journey, which we referred to as the data life cycle, uh, what the data life cycle, what is the data life cycle that our customers want, uh, to take their data through, to enable the end to end data journey. >>If you ask our customers, they want different types of analytics, uh, for their diverse user bases to help them implement their, their, their use cases while managed by a centralized security and governance later layer. Uh, in other words, um, the data life cycle to them provides multifunction analytics, uh, at each stage, uh, within the data journey, uh, that, uh, integrated and centralized, uh, security, uh, and governance, for example, uh, enterprise data consists of real time and transactional type type data. Examples include, uh, click stream data, web logs, um, machine generated, data chat bots, um, call center interactions, uh, transactions, uh, within legacy applications, market data, et cetera. We need to manage, uh, that data life cycle, uh, to provide real enterprise data insights, uh, for use cases around enhanced them, personalized customer experience, um, customer journey analytics next best action, uh, sentiment and churn analytics market, uh, campaign optimization, uh, mortgage, uh, processing optimization and so on. >>Um, we bring a diverse set of data then, um, and then enrich it with other data about our customers and products, uh, provide reports and dashboards such as customer 360 and use predictions from machine models to provide, uh, business decisions and, and offers of, uh, different products and services to customers and maintain customer satisfaction, um, by using, um, sentiment and churn analytics. These examples show that, um, the whole data life cycle is involved, um, and, uh, is in continuous fashion in order to meet these types of use cases, uh, using a single cohesive platform that can be, uh, that can be served by CDP, uh, the data, the Cloudera data platform. >>Okay. Uh, let's talk about, uh, some of the experiences, uh, from our customers. Uh, first we'll talk about Bunco suntan there. Um, is a major global bank headquartered in Spain, uh, with, uh, major operations and subsidiaries all over Europe and north and, and south America. Uh, one of its subsidiaries, something there UK wanted to revolutionize the customer experience with the use of real time data and, uh, in app analytics, uh, for mobile users, however, like many financial institutions send them there had a, he had a, had a large number of legacy data warehouses spread across many business use, and it's within consistent data and different ways of calculating the same metrics, uh, leading to different results. As a result, the company couldn't get the comprehensive customer insights it needed. And, uh, and business staff often worked on multiple versions of the truth. Sometime there worked with Cloudera to improve a single data platform that could support all its workloads, including self-service analytics, uh, operational analytics and data science processes, processing processing, 10 million transactions daily or 30,000 transactions per second at peak times. >>And, uh, bringing together really, uh, nearly two to two petabytes of data. The platform provides unprecedented, uh, customer insight and business value across the organization, uh, over 80 cents. And there has realized impressive, uh, benefits spanning, uh, new revenues, cost savings and risk reductions, including creating analytics for, for corporate customers with near real-time shopping behavior, um, and, and helping identify 7,000 new corporate, uh, customer prospects, uh, reducing capital expenditures by, uh, 3.2 million annually and decreasing operating expenses by, uh, 650,000, um, enabling marketing to realize, uh, 2.4 million in annual savings on, on cash, on commercial transactions, um, and protecting 3.7 million customers from financial crime impacts through 95, new proactive control alerts, improving risk and capital calculations to reduce the amount of money. It must set aside, uh, as part of a, as part of risk mandates. Uh, for example, in one instance, the risk team was able to release a $5.2 million that it had withheld for non-performing credit card loans by properly identifying healthy accounts miscategorized as high risk next, uh, let's uh, talk about, uh, Rabobank. >>Um, Rabobank is one of the largest banks in the Netherlands, uh, with approximately 8.3 million customers. Uh, it was founded by farmers in the late 19th century and specializes in agricultural financing and sustainability oriented banking, uh, in order to help its customers become more self-sufficient and, uh, improve their financial situations such as debt settlement, uh, rebel bank needed to access, uh, to a varied mix of high quality, accurate, and timely customer data, the talent, uh, to provide this insight, however, was the ability to execute sophisticated and timely data analytics at scale Rabobank was also faced with the challenge of, uh, shortening time to market. Uh, it needed easier access to customer data sets to ensure that they were using and receiving the right financial support at the right time with, with, uh, data quality and speed of processing. Um, highlighted as two vital areas of improvement, Rabobank was looking to incorporate, um, or create new data in an environment that would not only allow the organization to create a centralized repository of high quality data, but also allow them to stream and, uh, conduct data analytics on the fly, uh, to create actionable insights and deliver a strong customer experience bank level Cloudera due to its ability to cope with heavy pressures on data processing and its capability of ingesting large quantities of real time streaming data. >>They were able to quickly create a new data lake that allowed for faster queries of both historical and real time data to analyze customer loan repayment patterns, uh, to up to the minute transaction records, um, Robert bank and, and its customers could now immediately access, uh, the valuable data needed to help them understand, um, the status of their financial situation in this enabled, uh, rebel bank to spot financial disasters before they happened, enabling them to gain deep and timely insights into which customers were at risk of defaulting on loans. Um, having established the foundation of a modern data architecture Rabobank is now able to run sophisticated machine learning algorithms and, uh, financial models, uh, to help customers manage, um, financial, uh, obligations, um, including, uh, long repayments and are able to generate accurate, uh, current real liquidity. I refuse, uh, next, uh, let's uh, speak about, um, uh, OVO. >>Uh, so OVO is the leading digital payment rewards and financial services platform in Indonesia, and is present in 115 million devices across the company across the country. Excuse me. Um, as the volume of, of products within Obos ecosystem increases, the ability to ensure marketing effectiveness is critical to avoid unnecessary waste of time and resources, unlike competitors, uh, banks, w which use traditional mass marketing, uh, to reach customers over, oh, decided to embark on a, on a bold new approach to connect with customers via, uh, ultra personalized marketing, uh, using the Cloudera stack. The team at OVO were able to implement a change point detection algorithm, uh, to discover customer life stage changes. This allowed OVO, uh, to, uh, build a segmentation model of one, uh, the contextual offer engine Bill's recommendation algorithms on top of the product, uh, including collaborative and context-based filters, uh, to detect changes in consumer consumption patterns. >>As a result, OVO has achieved a 15% increase in revenue, thanks to this, to this project, um, significant time savings through automation and eliminating the chance of human error and have reduced engineers workloads by, by 30%. Uh, next let's talk about, uh, bank Bri, uh, bank Bri is one of the largest and oldest, uh, banks in Indonesia, um, engaging in, in general banking services, uh, for its customers. Uh, they are headquartered in, in Jakarta Indonesia, uh, PR is a well-known, uh, for its, uh, focused on micro-financing initiative initiatives and serves over 75 million customers through more than 11,000 offices and rural outposts, um, Bri needed to gain better understanding of their customers and market, uh, to improve the efficiency of its operations, uh, reduce losses from non-performing loans and address the rising concern around data security from regulators and consumers, uh, through enhanced fraud detection. This would require the ability to analyze the vast amounts of, uh, historical financial data and use those insights, uh, to enhance operations and, uh, deliver better service. >>Um, Bri used Cloudera's enterprise data platform to build an agile and reliable, uh, predictive augmented intelligence solution. Uh, Bri was now able to analyze 124 years worth of historical financial data and use those insights to enhance its operations and deliver better services. Um, they were able to, uh, enhance their credit scoring system, um, the solution analyzes customer transaction data, and predicts the probability of a customer defaulting on, on payments. Um, the following month, it also alerts Bri's loan officers, um, to at-risk customers, prompting them to take the necessary action to reduce the likelihood of the net profit lost, uh, this resulted in improved credit, improved credit scoring system, uh, that cut down the approval of micro financing loans, uh, from two weeks to two days to, to two minutes and, uh, enhanced fraud detection. >>All right. Uh, this example shows a tabular representation, uh, the evolution of a customer retention use case, um, the evolution of data and analytics, uh, journey that, uh, that for that use case, uh, from aware, uh, text flirtation, uh, to optimization, to being transformative, uh, with every level, uh, data sources increase. And, uh, for the most part, uh, are, are less, less standard, more dynamic and less structured, but always adding more value, more insights into the customer, uh, allowing us to continuously improve our analytics, increase the velocity of the data we ingest, uh, from, from batch, uh, to, uh, near real time, uh, to real-time streaming, uh, the volume of data we ingest continually increases and we progress, uh, the value of the data on our customers, uh, is continuously improving, allowing us to interact more proactively and more efficiently. And, and with that, um, I would, uh, you know, ask you to consider and assess if you are using all the, uh, the data available to understand, uh, and service your customers, and to learn more about, about this, um, you know, visit cloudera.com and schedule a meeting with Cloudera to learn more. And with that, thank you for your time. And thank you for listening.

(Upbeat music) >> Welcome to theCUBE's presentation of the AWS Startup Showcase, the next big thing in AI, security, and life sciences. We're here in the security track, and in this segment we feature Cequence Security. I'm your host, Dave Vellante, and today, we're joined by Subbu Iyer, who's the vice president of products at Cequence Security. And we're going to discuss the importance of rapidly discovering and addressing common API security gaps. We live in this API economy, and there's dangers out there. Welcome, Subbu. Great to have you on the Cube. >> Thanks, Dave. I'm happy to be here. >> Okay, every week, there's some other report in the paper, in the news, high profile security breaches. We all know about the Experian breach, the Clubhouse, a pretty popular app, and many, many others. But you know what's perhaps more scary is the ones we don't hear about, and there are a lot of those. APIs are increasingly targeted by cyber criminals as a weak link to steal data and commit fraud. So, Subbu, in thinking about your customers and how they're using your solution, what are some of the patterns that you're discerning, and how are you addressing this problem within your community? >> Yeah, APIs are a very common avenue for exploiting vulnerabilities in applications, and what we are discovering amongst our customers is that there are elementary gaps that are being left behind in APIs. For instance, APIs that are completely unauthenticated, and it's practically like leaving your front door open and allowing anybody to walk in. I mean, there are APIs that aren't authenticated, APIs that are exposing sensitive information, like credit card numbers or social security numbers completely out in the clear. Or APIs that are using weak forms of authentication that are very easily bypassed. The OWASP API top ten is actually a pretty good handy list for people to look up, and those are, we see many of those as being very commonly seen vulnerabilities in APIs. >> Yeah. I mean, your adversaries, they're experts at automation. They knock on that door, and if it's open, they come right in. (Laughs) They don't even have to manually do it. They just automatic. So, talk a little bit more about that, that problem of poor API visibility in this world in which we now live. >> Yeah. You nailed it. I mean, visibility is the number one thing that everybody should be thinking about. In an age where APIs are ubiquitous, like everything talks to everything else by APIs, lack of knowledge of how many APIs there are out there that a customer has exposed is the number one challenge that anybody should start with. Getting your arms around the problem statement of how many APIs do I have that are publicly exposed, are privately exposed to other organizations, is really where it all starts. And once you have discovered all those APIs, then you basically look at what risk those APIs pose to you, like how many of those APIs aren't authenticated? How many of those APIs are using very weak forms of authentication or are exposing sensitive information on, you know, subject to some of the other commonly seen risks? >> You know, authentication is the other area. And not just humans. You know, talking about machines as well. So, this is a critical weak link, and it's fraught with complexity. You've got multiple devices. You've got service connections. And it's very error prone. How much of a problem is this, and what can we do about it? >> Authentication is actually the most basic and the most commonly seen vulnerability or flaw in APIs. The most common flaw that we see in APIs is the problem of APIs not having any authentication at all or having very weak forms of authentication. To kind of go back to our front door analogy, that's like either leaving your front door completely unlocked, or leaving your front door locked and leaving the key under the doormat hoping that nobody is going to pull up the doormat and find the key in there. Like, it's pretty much the equivalent of that for APIs that we see. That's really the gambit of like authentication related issues that we see, like in that ballpark of either weak forms of authentication that attackers really don't have to even break a sweat to kind of find their way around them and walk in. >> You know, I recently wrote about this. I wanted to ask you about another disturbing trend that we see, and that's, you know, adversaries, they're looking in our environments, and they're stealth. You know, they're living off the land and using your tools against you so you don't even see them a lot of times. While they're there, they're exfiltrating troves of very valuable information. In one study I read, they were committing fraud and identity theft, but they were also stealing sensitive data so they could act on it, like front running a trade. And so... And then they would hold that data, that sensitive data. Another example, it was healthcare data, private information. And they're hold it on reserve, so to speak, so they could extort victims once they're discovered and there's an incident response. So this exposure to sensitive data, it's an enormous problem, and I wonder if you could share your thoughts on this topic and how to help remediate it. >> Yep. Sensitive data exposure is another one of the OWASP API top ten, and something that we see pretty often with APIs. And you're right. Adversaries basically look for avenues where they can exploit these sensitive data flaws that an API may have. Common ways of doing that are maybe the attacker discovers or finds the mobile application for that particular application. It may be a retail app, a finance app. And the user may create a valid account in there, so get in there as a valid account, and see the APIs that are being communicated by the mobile app with the API backend, and then see if they can retrieve other people's information or that same API, by changing the user ID or other tokens that they are sending back. And if they are able to break in and they're actually able to get other users' information, that's basically exfiltration, data exfiltration. And they just run that in a script and are able to exfiltrate lots of data from the API backend if the access control on the backend is weak and this API is really not protected very well. So, one of the key things that we do in Cequence is provide visibility to our customers about what form, which APIs are exposing sensitive data information. What sensitive information are they? So, are they credit card numbers, social security numbers, some other proprietary identifiers that a customer may have, and really how are they leaking this information? Is it in the response body? Is it in the response header? And so on. So, we really give them the ability to hone in on where the leakage is happening. >> Okay. So, full visibility. Maybe talk a little bit more about that. I mean, can you share a little bit about, you know, your secret sauce, if you will? Your kind of unique approach to solving this problem? >> Yeah. That's a good questions. So, Cequence is in the business of providing continuous visibility and monitoring and protection to customers for their public facing web applications and APIs. And we do this by essentially providing the ability, to start with, to customers to discover all of their public facing APIs. And we do that by essentially tapping into their network at various points. We can tap into an API gateway, a load balancer, or really deep into their microservices applications, to tap into their modern API-based applications as well. And by tapping into multiple sources within their environment, we are essentially gleaning a complete picture of what their application attack surface looks like. So, all of these become what we call sensors that essentially communicate information back to a central repository and aggregate all that information together, and then produce this visibility of saying you have however many APIs. And then that's where a lot of analysis happens to see who's communicating all of these APIs. Are we getting traffic from external sources? internal sources? How are the API communications happening? Is it in clear text? And so on. And that's where the visibility really happens. >> And these so-called sensors, they're sort of embedded as part of your service. So, I'm acquiring a service from you, correct? Maybe you could describe a little bit more about how I interact with your product portfolio. >> Yep, yep. So, our technology is flexible enough that it can be completely deployed as a software as a service, so needing nothing to be deployed on a customer's premises at all. Or it can, or we are flexible enough to actually support on premises deployments for some of our larger customers like financial customers or other data privacy related customers who would rather have this infrastructure on their premises. And these sensors are really these little modules that go in their network environment, so they're easy to deploy, easy to integrate with any network-based options like load balancers or firewalls or API gateways. And really, the backend can be consumed either as a software based application like a docker or Kubernetes application on the customer's premises, or consumed within the sequence cloud, so needing absolutely nothing to be managed or maintained by the customer at all. So, really the customer, to start with, essentially comes to Cequence and says, "Hey, how do I get an environment up and running where I can play with my, where I can discover my APIs?" And we can spin up an environment in our cloud in a matter of minutes or hours. And before you know it, we can drop a couple of sensors in their environment, and we are discovering their APIs. >> And then what? So, if you... You discover some APIs, the key under the doormat, so to speak, what do I then do as a customer? How do you help me accelerate that remediation? >> Excellent. And that's where one of the important aspects of our product called mitigation comes in the picture. Mitigation is a remediation action where a customer can actually take action in the run time to actually stop some of this back traffic from happening. For instance, if you see an API that... One of the common things that we discover in APIs are what we call shadow APIs. So, when we talked about API visibility, what a customer discovers is all of their known APIs and unknown APIs. So known APIs are APIs that they know about. Oh, that's my payment API. This is my billing API, and so on. And they also discover APIs that they did not know exist, like a newer version of an API that the development team has exposed. And they go, "Wait a minute. When, how did that get exposed to the public? We haven't even done a security audit of this thing." So, if they see APIs like this that should not have been made public but are public and are being used, they can use Cequence to essentially block traffic to those APIs, these unreleased APIs, or these hidden APIs that should never have gone public but are public, either because of unintentional mistakes on somebody's part or because of certain compliance loopholes or something that these were made public. So, we can take action and prevent traffic from hitting these APIs. >> And I would imagine, Subbu, that, I mean, every environment's different. I mean, I would imagine people make the same mistakes across different environments, but every environment is really a snowflake. I mean, it's an individual, you know, configuration. And so... But I wonder, are you seeing, well, sort of what was my first question, what kind of patterns are you seeing? But are you seeing, you know, customers exposed in sort of the same areas? Or are you seeing like I'm describing, every situation is different? >> We do see situations being different. >> Hmm. >> I have seen environments where the production environment had a weaker security posture than a development environment. >> Hmm. >> That was presumably because the development environment was running a newer version of their application, so it had plugged some of these API gaps, was not leaking sensitive information. But the production environment is running an older version. So, it had flaws that the development environment did not. And I've seen vice versa as well. So, really it depends upon like how their CI/CD processes, how soon are they able to get these applications into production, and so on. And accordingly, they put in actions in, let's say, in the appropriate environment, the dev environment or the prod environment to stop these attacks from hitting that environment. >> So, if a customer comes to you, you know, fresh, you haven't worked with that customer, and then you deploy these sensors and you help them sort of clean up their operation. And presumably they want to keep working with you because their environment is constantly changing, are they then... You know, it's kind of a cliche, but shifting left, you know, where they're building this in to their development process as opposed to saying, "Okay, we've just deployed. Now let's call Cequence in." Right? Are you able to align with the development life cycle more closely? >> Yeah. Yeah, yeah. That's an ideology that we do here at Cequence. What we say is shift left while shielding right. What that means is, yes, shifting left is an important strategy for you to essentially take these actions earlier on in the development process before these APIs become public. But one of the key tenets of application security is to shield your applications from bad traffic. Shield your applications from these attackers who are trying to enumerate these IDs and trying to exfiltrate information. So, you need to protect your applications from bad traffic. So, while shielding the right, we allow customers to start shielding left so that they can start testing some of these APIs before they go into production. So, before these APIs even become, see the light of day, let's say a newer version of an API, we are working with customers in that journey so that they can find this sooner and sooner in the development life cycle. So, yep. We absolutely see that as an evolution for customers. >> Thank you for that. So, I got two more questions for you. The first one is kind of a fun question that theCUBE team, you know, wanted to ask. We're asking all the startups. Remember, the event, it's all about cloud scale. And so, of course, when you launch a company, a startup, it's exciting time. You're innovating, developing new products, moving fast, breaking things, helping customers out, disrupting. All those cool things. Growing the company. You know, increasing revenue. Great. But there's more even. And so what we're asking folks like yourself, Subbu, is what is your defining contribution to the future of cloud scale? >> Yep. So, cloud scale is not possible without a digital transformation of applications. So, applications have to be digitally transformed so that they are ready for the modern cloud age. And in order to do that, applications have to become API first. They have to understand APIs. They have to communicate to other applications why APIs and allow other applications to communicate by APIs. So, to truly achieve cloud scale, digital transformation is an absolute must. And we are playing our part in that journey for customers in digital transformation by allowing them to go on to their digital transformation journeys and allowing cloud scale by protecting their APIs, allowing them to discover their APIs and protecting their APIs, allowing them to reach cloud scale. >> Great. Thank you for that. Now, let's summarize, and I wonder if you can sort of bring us home, and give us your thoughts. I mean, there's a balancing act that we have to do between you want to tap into the API trend and the value that it brings, but at the same time, you got to mitigate the risks associated with that. And just give us a summary on the right prescription. >> Yep. So, to kind of bring it all together, right? API security is top of minds for many (indistinct) across the board. And it all starts with API visibility. You cannot protect what you cannot see. So, you have to be able to discover your entire API attack surface so you know what's going on with your APIs. And then you put in shield right strategies where you essentially are blocking the bad traffic from hitting your applications. That's basically the logical evolution from, you know, discovering the bad traffic in your environment. First, visibility, and then protect what is going on. And then start shielding left, shifting left, by essentially being able to take these actions sooner in your development life cycle so that some of these bad traffic can never possibly hit your applications because you have shifted left. >> Excellent. Well, Subbu, thanks so much for coming on theCUBE today. It was great to have you. Great stuff. >> Thank you, Dave, for having me. This was great. Thank you so much. >> Our pleasure. And thank you for watching theCUBE's presentation of the AWS Startup Showcase, the next big thing in AI, security, and life sciences. Keep it right there for more great content. (upbeat music begins) (upbeat music fades out)

(upbeat music) >> Welcome to theCUBE's coverage of HPE Discover 2021. I'm Lisa Martin. Robert Christiansen joins me, one of our alumni the VP of Strategy in the Office of the CTO at HPE. Robert, it's great to see you, welcome back to the program. >> It's nice being here, Lisa. Thank you so much for having me. >> So here we are still in this virtual world. Things are opening up a little bit, which is nice but one of the things I'm excited to talk to you about today is Edge to Cloud from the customer's perspective. Obviously, that's why HPE does what it does for its customers. So let's talk about some of the things that you see from your perspective, with respect to data. We can't have a Cube conversation without talking about data, there's more and more of it, value but getting access to it quickly, getting access to it in real-time and often cases to make data-driven decisions is a challenging thing to do. Talk to me about what you see from the customer's lens. >> Well, the customer at a very highest level from the board level on down they're saying, "Hey, what is our data strategy? How are we going to put the value of data in place? Are we going to have it manifest its value in an internal fashion where it makes us run better as an organization? Can we get cost improvements? Can we move quicker with that? And then can we monetize that data if it's like very specific to an industry like healthcare or pharma or something like that? Can we expose that data to the rest of the world and give them access into what we call like data sets?" And there's a lot of that going on right now too. So we're seeing these two different angles about how they're going to manage and control that data. And you were talking about, and you mentioned it, you know the Edge related focus around that. You know, the Edges where business is done is where people actually do the transaction whether it's in a healthcare like in a hospital or a manufacturing facility et cetera. And then, but that data that they're using at that location is really important to make a decision at that location. They can't send it back to a Cloud. They can't send it back to someplace, wait for a decision to happen and then shoot it back again and say, "Hey, stop the production line because we found a defect." You need to act at that moment which the clients are saying, "Hey, can you improve my reliability? Can you give me better SLS? Can you improve the quality of my products? Can you improve healthcare in a hospital by immediate decisions?" And that is a data problem. And that requires the movement of compute and networking and storage and fundamentally the core piece of HPE's world. But in addition to that, the software necessary to take the action on that data when they detect that there's some action that needs to be taken. >> And I mentioned a minute ago, you know real-time and we've learned in the last 15 months plus. One of the things we learned is for a lot of cases, access to real-time data is no longer a nice to have. It's really going to be something, an element that separates those that succeed versus those that aren't as competitive. But I want to talk about data from a consumption perspective consumers, producers, obviously, meeting to ensure that the data consumers have what they need, what is it? What is your thought when you talk with customers, the consumers versus the producers? >> Yeah, that's a great question, Lisa. One of the key fundamental areas that HPE and the Office of the CTO has really been focused on over the last six months is something that we call data spaces and that is putting in place a platform, a set of services that connect data consumers with data producers. And when you think about that, that really isn't nothing new. I mean, you could go all the way back, if you've been around for a while remember the company called TRW and they used to have credit reporting, and they used to sell that stuff. And then it moved into Experian and those things. But you've got Bloomberg and next LexisNexis and all these companies that sell data. And they've been doing it, but it's very siloed. And so the explosion of data, the valuableness the value of the data for the consumers of it has put the producers in a position where they can't readily be discovered. And whether it be a private source of data like an IoT device and an industrial control, or a set of data that might say, "Hey, here's credit card for our data on a certain geography." Those sets need to be discovered, curated, and be made available to those who would want that. You know, for example, the folks that want to know how IoT device is working inside an industrial control or a company who's trying to lower their fraud rates on credit card transactions, like in stadiums or something like that. And so this discoverability in this space, or what you just talked about is such a core piece of what we're working on right now. And we haven't, our strategy is not only to just work on what HPE has to bring that and manifest that to the marketplace. But more importantly, how are we working with our partners to really bridge that gap and bring that next generation of services to those clients that can make those connections. >> So connecting and facilitating collaboration, absolutely key, as well as that seamless flow of data sharing without constraints. How are customers working with HPE and some of your partners to be able to create a data strategy, launch it, and start gleaning value from data faster than they can before? (Robert chuckles) >> This is the big question because it's a maturity curve. Organizations are in various states of what we call data maturity or data management maturity. They can be in very early stages. You know what we consider, you know, they just more worried about just maintaining the lights on DR strategies and make sure that data doesn't go away versus all the way through a whole cycle where they're actually governing it and putting it into what I call those discoverable buckets that are made available. And there's a whole life cycle about that. And so we see a big opportunity here for our A&PS and other professional services organizations to help people get up that maturity curve. But they also have to have the foundational tools necessary to make that happen. This is really where the Ezmeral product line or software applications really shines being able to give that undercarriage that's necessary to help that data maturity and the growth of that client to meet those data needs. And we see the data fabric being a key element to that, for that distributed model, allowing people to get access and availability to have a highly redundant, highly durable data fabric and then to build applications specifically as data-intensive applications on top of that with the Ezmeral platform all the way into our GreenLake solutions. So it's quite a journey here, Lisa. I want to just, point to the fact that HPE has done a really, really good job of positioning itself for the explosion of all of these data-intensive AI/ML workloads that are making their way into every single conversation every single enterprise to this day that wants to take advantage of the value of the data they have and to augment that data through other sources. >> One, when you think about data-intensive applications the first one that pops into my mind is Uber. And it's one of those applications that we just expect. We kind of think of as a taxi service when really it's logistics and transportation, but all of the data on the backend that it is organizing to find the ride for me at my location to take me where I'm going. The explosion of data-intensive applications is great but there's also so much more demand from consumers whether we're in business or we're consuming in our personal lives. >> It's so true and that's a very popular example. And you know, you think about the real-time necessity of what's the traffic patterns at the time I order my thing. Is it going to route me the right way? That's a very real consumer facing one, but if we click into our clients and where HPE very much is like the backbone of the global economy. We provide probably one third of the compute for the global economy and it's a staggering stat if you really think about it. Our clients, I was just talking with a client here earlier, very, very large financial services company. And they have 1200 data sets that have been selling to their clients globally. And a lot of these clients want to augment that data with their existing real-time data to come up with a solution. And so they merge it and they can determine some value through a model, an AI model. And so we're working hand-in-hand with them right now to give them that backbone so that they can deliver data sets into these other systems and then make sure they get controlled and secured. So that the company we're working with, our client has a deep sense of security that that data set is not going to find itself out into the wild somewhere. And uncontrolled for a number of reasons, from security and governance mind. But the number of use cases, Lisa are as infinite as the number of opportunities for people see value in business today. >> When you're talking about 1200 data sets that a company is selling, and of course there are many, many data sets that many types of companies consume. How do you work with them to ensure that they don't just proliferate silos, but that they get more of a unified data repository that they can act on? >> Yeah, that's a great question. A key tenant of the strategy at HPE is Open-source. So we believe in a hybrid, multi-Cloud environment meaning that as long as we all agree that we are going to standardize on Open-source technologies and APIs, we will be able to write and build applications that can natively run on any abstract platform. So for example, it's very important that we containerize, for example, and we use storage and data tools that adhere to Open standards. So if you think about that, if you write a Spark application you want that Spark application potentially to run on any of the hyperscalers, the Amazon's or the Microsoft to GCPS, or you want it to run on-premises and specifically like on HPE equipment. But the idea here is I consider one of our clients right now. I mean, think about that. One of our clients specifically ask that question that you just said. They said, "Hey, we are building out this platform, this next generation platform. And we don't want the lock-in. We want to be, we want to create that environment where that data and the data framework." So they use very specific Open -source data frameworks and they open, they use very specific application frameworks the software from the Open-source community. We were able to meet that through the Ezmeral platform. Give them a very high availability, five nines high availability, redundant, redundant geographically to geographic data centers to give them that security that they're looking for. And because of that, it's opened so many other doors for us to walk in with a Cloud strategy that is an alternative, not just the one bet to public Cloud but you haven't other opportunity to bring a Cloud strategy on-premises that is compatible with Cloud-native activities that are going on in the public Cloud. And this is at the heart of HPE strategy. I think it's just, it's been paying off. It continues to pay off. We just keep investing and keep moving down that path. I think we're going to be doing really well. >> It sounds to me that the strategy that HP is developing is highly collaborative and synergistic with your customers. Talk to me a little bit about that, especially in the last year, as we've seen a massive acceleration in digital transformation about the rapid pivot to work from home, the necessity to collaborate electronically. Talk to me a little bit about that yin and yang with HPE and its customers in terms of your strategy. >> Yeah, well, I think when COVID hit one of the very first things that just took off with VDI. Rohit Dixon and I were talking on a podcast we had earlier around the work from home strategy that was implemented almost immediately. Well, we had it already in the can, we already were doing it for many clients already but it went from like a three priority to a 12, 10 being the max. Super, super charged up on how do we get work from home secured, work from home applications and stuff in the hands of people doing, you know, when data sensitivity is super important, VDI kicks in that's on that side. But then if you start looking at the digital transformation that has to happen in the supply chain that's going on right now. The opening up of our economies it's been various starts and stops if you look around the globe. The supply chains have absolutely gone under a huge amount of pressure, because, unlike in the United States, everybody just wants everything now because things are starting to open up. I was talking to a meat packing company and a restaurant business a little while ago. And they said, "Everybody wants to order the barbecue. Now we can't get the meat for the barbecues 'cause everybody's going to the barbecues." And so the supply, this is a multi-billion dollar industry supplying meat to all of the rest of the countries and stuff like that. And so they don't have optics into that supply chain today. So they're immediately having to go through a digitization process, the transformation in something as what you would call as low tech as delivering meat. So no industry is immune, none anywhere in this whole process. And it will continue to evolve as we exit and change how we live our life going into these next couple of years. I think it's going to be phenomenal just to watch. >> Yeah, it's one of the things I call a COVID catalyst some of the silver linings that have come out of this 'cause I wouldn't have thought of the meatpacking industry as a technology field as well, but now thanks to you, I will. Last question for you. When customers in this dynamic world in which we're still living talk about Edge to Cloud are they working with you to develop a Cloud initiatives, Cloud mandates, Cloud everywhere? And if so, how do you help them start? >> Yeah, that's a great question. So again, it's like back into the data model, everybody has a different degree or a starting point that they will engage us with a strategy but specifically with what you're talking about. Almost everybody already has a Cloud strategy. So they may be at different maturity levels with that Cloud strategy. And there's almost always a Cloud group. Now, historically HPE has not had much of a foot in the Cloud group because they never really historically looked at us says that HPE is a Cloud company. But what's happened over the last couple of years with the acceleration of the acceptance of Cloud on-premises and GreenLake, specifically, and the introduction of Ezmeral and the Cloud-native infrastructure services and past layer stuff that's coming up through the Ezmeral product into our clients. It's immediately opened the door for conversations around Cloud that is available for what is staying on-premises which is in excess of 70% of the applications today. Now, if you were to take that now and extend that into the Edge conversation, what if you were able to take a smaller form factor of a GreenLake Cloud and push it more closer to an Edge location while still giving the similar capabilities, Cloud-native functions that you had before? When we're provocative with clients in that sense they suddenly open up and see the art of the possible. And so this is where we are really, really breaking down a set of paradigms of what's possible by introducing, you know, not just from the Silicon all the way up but the set of services all the way to the top of stack to the actual application that they're going to be running. And we say, "Hey, we can offer it to you in as a pay as you go model, we can get you the consumption models that are necessary, that lets you buy at the same way as the Cloud offers it. But more importantly, we'll be able to run it for you and provide you an abstraction out of that model. So you don't have to send your people out into the field to do these things. We have the software, the tools, and the systems necessary to manage it for you." But the last part is I want to be really really focused on when clients are writing that application for the Edge that matters. They are putting it into new Cloud-native architectures containers, microservices, they're using solid pipelines development pipelines, they've implemented what they call their DevOps or their DataOps practices in field, in country, if you would say. That's where we shine. And so we had a really, really good conversation start there. And so how we start that is we arrive with a set of blueprints to help them establish what that roadmap looks like. And then our professional services staff, or A&PS groups around the globe are really really set up well to help them take that trip. >> Wow, that's outstanding, Robert. We could have a whole conversation on HPE's transformation. Internet itself that was my first job in tech was at Hewlett Packard back in the day. But this has been really interesting, really getting it your vision of the customer's experience and the customer's perspective from the Office of the CTO. Great to talk to you, Robert. Thank you for sharing all that you did. This could have been a Part 2 conversation. >> Well, I'm hopeful then that we'll do Part 3 and 4 here as the months go by. So I look forward to seeing you again, Lisa. >> Deal, that's a deal. All right. >> All right. >> For Robert Christiansen, I'm Lisa Martin. You're watching theCUBE's coverage of HPE Discover 2021. (upbeat music)

(upbeat music) >> Welcome to theCUBE's coverage of HPE Discover 2021. I'm Lisa Martin. Robert Christiansen joins me, one of our alumni the VP of Strategy in the Office of the CTO at HPE. Robert, it's great to see you, welcome back to the program. >> It's nice being here, Lisa. Thank you so much for having me. >> So here we are still in this virtual world. Things are opening up a little bit, which is nice but one of the things I'm excited to talk to you about today is Edge to Cloud from the customer's perspective. Obviously, that's why HPE does what it does for its customers. So let's talk about some of the things that you see from your perspective, with respect to data. We can't have a Cube conversation without talking about data, there's more and more of it, value but getting access to it quickly, getting access to it in real-time and often cases to make data-driven decisions is a challenging thing to do. Talk to me about what you see from the customer's lens. >> Well, the customer at a very highest level from the board level on down they're saying, "Hey, what is our data strategy? How are we going to put the value of data in place? Are we going to have it manifest its value in an internal fashion where it makes us run better as an organization? Can we get cost improvements? Can we move quicker with that? And then can we monetize that data if it's like very specific to an industry like healthcare or pharma or something like that? Can we expose that data to the rest of the world and give them access into what we call like data sets?" And there's a lot of that going on right now too. So we're seeing these two different angles about how they're going to manage and control that data. And you were talking about, and you mentioned it, you know the Edge related focus around that. You know, the Edges where business is done is where people actually do the transaction whether it's in a healthcare like in a hospital or a manufacturing facility et cetera. And then, but that data that they're using at that location is really important to make a decision at that location. They can't send it back to a Cloud. They can't send it back to someplace, wait for a decision to happen and then shoot it back again and say, "Hey, stop the production line because we found a defect." You need to act at that moment which the clients are saying, "Hey, can you improve my reliability? Can you give me better SLS? Can you improve the quality of my products? Can you improve healthcare in a hospital by immediate decisions?" And that is a data problem. And that requires the movement of compute and networking and storage and fundamentally the core piece of HPE's world. But in addition to that, the software necessary to take the action on that data when they detect that there's some action that needs to be taken. >> And I mentioned a minute ago, you know real-time and we've learned in the last 15 months plus. One of the things we learned is for a lot of cases, access to real-time data is no longer a nice to have. It's really going to be something, an element that separates those that succeed versus those that aren't as competitive. But I want to talk about data from a consumption perspective consumers, producers, obviously, meeting to ensure that the data consumers have what they need, what is it? What is your thought when you talk with customers, the consumers versus the producers? >> Yeah, that's a great question, Lisa. One of the key fundamental areas that HPE and the Office of the CTO has really been focused on over the last six months is something that we call data spaces and that is putting in place a platform, a set of services that connect data consumers with data producers. And when you think about that, that really isn't nothing new. I mean, you could go all the way back, if you've been around for a while remember the company called TRW and they used to have credit reporting, and they used to sell that stuff. And then it moved into Experian and those things. But you've got Bloomberg and next LexisNexis and all these companies that sell data. And they've been doing it, but it's very siloed. And so the explosion of data, the valuableness the value of the data for the consumers of it has put the producers in a position where they can't readily be discovered. And whether it be a private source of data like an IoT device and an industrial control, or a set of data that might say, "Hey, here's credit card for our data on a certain geography." Those sets need to be discovered, curated, and be made available to those who would want that. You know, for example, the folks that want to know how IoT device is working inside an industrial control or a company who's trying to lower their fraud rates on credit card transactions, like in stadiums or something like that. And so this discoverability in this space, or what you just talked about is such a core piece of what we're working on right now. And we haven't, our strategy is not only to just work on what HPE has to bring that and manifest that to the marketplace. But more importantly, how are we working with our partners to really bridge that gap and bring that next generation of services to those clients that can make those connections. >> So connecting and facilitating collaboration, absolutely key, as well as that seamless flow of data sharing without constraints. How are customers working with HPE and some of your partners to be able to create a data strategy, launch it, and start gleaning value from data faster than they can before? (Robert chuckles) >> This is the big question because it's a maturity curve. Organizations are in various states of what we call data maturity or data management maturity. They can be in very early stages. You know what we consider, you know, they just more worried about just maintaining the lights on DR strategies and make sure that data doesn't go away versus all the way through a whole cycle where they're actually governing it and putting it into what I call those discoverable buckets that are made available. And there's a whole life cycle about that. And so we see a big opportunity here for our A&PS and other professional services organizations to help people get up that maturity curve. But they also have to have the foundational tools necessary to make that happen. This is really where the Ezmeral product line or software applications really shines being able to give that undercarriage that's necessary to help that data maturity and the growth of that client to meet those data needs. And we see the data fabric being a key element to that, for that distributed model, allowing people to get access and availability to have a highly redundant, highly durable data fabric and then to build applications specifically as data-intensive applications on top of that with the Ezmeral platform all the way into our GreenLake solutions. So it's quite a journey here, Lisa. I want to just, point to the fact that HPE has done a really, really good job of positioning itself for the explosion of all of these data-intensive AI/ML workloads that are making their way into every single conversation every single enterprise to this day that wants to take advantage of the value of the data they have and to augment that data through other sources. >> One, when you think about data-intensive applications the first one that pops into my mind is Uber. And it's one of those applications that we just expect. We kind of think of as a taxi service when really it's logistics and transportation, but all of the data on the backend that it is organizing to find the ride for me at my location to take me where I'm going. The explosion of data-intensive applications is great but there's also so much more demand from consumers whether we're in business or we're consuming in our personal lives. >> It's so true and that's a very popular example. And you know, you think about the real-time necessity of what's the traffic patterns at the time I order my thing. Is it going to route me the right way? That's a very real consumer facing one, but if we click into our clients and where HPE very much is like the backbone of the global economy. We provide probably one third of the compute for the global economy and it's a staggering stat if you really think about it. Our clients, I was just talking with a client here earlier, very, very large financial services company. And they have 1200 data sets that have been selling to their clients globally. And a lot of these clients want to augment that data with their existing real-time data to come up with a solution. And so they merge it and they can determine some value through a model, an AI model. And so we're working hand-in-hand with them right now to give them that backbone so that they can deliver data sets into these other systems and then make sure they get controlled and secured. So that the company we're working with, our client has a deep sense of security that that data set is not going to find itself out into the wild somewhere. And uncontrolled for a number of reasons, from security and governance mind. But the number of use cases, Lisa are as infinite as the number of opportunities for people see value in business today. >> When you're talking about 1200 data sets that a company is selling, and of course there are many, many data sets that many types of companies consume. How do you work with them to ensure that they don't just proliferate silos, but that they get more of a unified data repository that they can act on? >> Yeah, that's a great question. A key tenant of the strategy at HPE is Open-source. So we believe in a hybrid, multi-Cloud environment meaning that as long as we all agree that we are going to standardize on Open-source technologies and APIs, we will be able to write and build applications that can natively run on any abstract platform. So for example, it's very important that we containerize, for example, and we use storage and data tools that adhere to Open standards. So if you think about that, if you write a Spark application you want that Spark application potentially to run on any of the hyperscalers, the Amazon's or the Microsoft to GCPS, or you want it to run on-premises and specifically like on HPE equipment. But the idea here is I consider one of our clients right now. I mean, think about that. One of our clients specifically ask that question that you just said. They said, "Hey, we are building out this platform, this next generation platform. And we don't want the lock-in. We want to be, we want to create that environment where that data and the data framework." So they use very specific Open -source data frameworks and they open, they use very specific application frameworks the software from the Open-source community. We were able to meet that through the Ezmeral platform. Give them a very high availability, five nines high availability, redundant, redundant geographically to geographic data centers to give them that security that they're looking for. And because of that, it's opened so many other doors for us to walk in with a Cloud strategy that is an alternative, not just the one bet to public Cloud but you haven't other opportunity to bring a Cloud strategy on-premises that is compatible with Cloud-native activities that are going on in the public Cloud. And this is at the heart of HPE strategy. I think it's just, it's been paying off. It continues to pay off. We just keep investing and keep moving down that path. I think we're going to be doing really well. >> It sounds to me that the strategy that HP is developing is highly collaborative and synergistic with your customers. Talk to me a little bit about that, especially in the last year, as we've seen a massive acceleration in digital transformation about the rapid pivot to work from home, the necessity to collaborate electronically. Talk to me a little bit about that yin and yang with HPE and its customers in terms of your strategy. >> Yeah, well, I think when COVID hit one of the very first things that just took off with VDI. Rohit Dixon and I were talking on a podcast we had earlier around the work from home strategy that was implemented almost immediately. Well, we had it already in the can, we already were doing it for many clients already but it went from like a three priority to a 12, 10 being the max. Super, super charged up on how do we get work from home secured, work from home applications and stuff in the hands of people doing, you know, when data sensitivity is super important, VDI kicks in that's on that side. But then if you start looking at the digital transformation that has to happen in the supply chain that's going on right now. The opening up of our economies it's been various starts and stops if you look around the globe. The supply chains have absolutely gone under a huge amount of pressure, because, unlike in the United States, everybody just wants everything now because things are starting to open up. I was talking to a meat packing company and a restaurant business a little while ago. And they said, "Everybody wants to order the barbecue. Now we can't get the meat for the barbecues 'cause everybody's going to the barbecues." And so the supply, this is a multi-billion dollar industry supplying meat to all of the rest of the countries and stuff like that. And so they don't have optics into that supply chain today. So they're immediately having to go through a digitization process, the transformation in something as what you would call as low tech as delivering meat. So no industry is immune, none anywhere in this whole process. And it will continue to evolve as we exit and change how we live our life going into these next couple of years. I think it's going to be phenomenal just to watch. >> Yeah, it's one of the things I call a COVID catalyst some of the silver linings that have come out of this 'cause I wouldn't have thought of the meatpacking industry as a technology field as well, but now thanks to you, I will. Last question for you. When customers in this dynamic world in which we're still living talk about Edge to Cloud are they working with you to develop a Cloud initiatives, Cloud mandates, Cloud everywhere? And if so, how do you help them start? >> Yeah, that's a great question. So again, it's like back into the data model, everybody has a different degree or a starting point that they will engage us with a strategy but specifically with what you're talking about. Almost everybody already has a Cloud strategy. So they may be at different maturity levels with that Cloud strategy. And there's almost always a Cloud group. Now, historically HPE has not had much of a foot in the Cloud group because they never really historically looked at us says that HPE is a Cloud company. But what's happened over the last couple of years with the acceleration of the acceptance of Cloud on-premises and GreenLake, specifically, and the introduction of Ezmeral and the Cloud-native infrastructure services and past layer stuff that's coming up through the Ezmeral product into our clients. It's immediately opened the door for conversations around Cloud that is available for what is staying on-premises which is in excess of 70% of the applications today. Now, if you were to take that now and extend that into the Edge conversation, what if you were able to take a smaller form factor of a GreenLake Cloud and push it more closer to an Edge location while still giving the similar capabilities, Cloud-native functions that you had before? When we're provocative with clients in that sense they suddenly open up and see the art of the possible. And so this is where we are really, really breaking down a set of paradigms of what's possible by introducing, you know, not just from the Silicon all the way up but the set of services all the way to the top of stack to the actual application that they're going to be running. And we say, "Hey, we can offer it to you in as a pay as you go model, we can get you the consumption models that are necessary, that lets you buy at the same way as the Cloud offers it. But more importantly, we'll be able to run it for you and provide you an abstraction out of that model. So you don't have to send your people out into the field to do these things. We have the software, the tools, and the systems necessary to manage it for you." But the last part is I want to be really really focused on when clients are writing that application for the Edge that matters. They are putting it into new Cloud-native architectures containers, microservices, they're using solid pipelines development pipelines, they've implemented what they call their DevOps or their DataOps practices in field, in country, if you would say. That's where we shine. And so we had a really, really good conversation start there. And so how we start that is we arrive with a set of blueprints to help them establish what that roadmap looks like. And then our professional services staff, or A&PS groups around the globe are really really set up well to help them take that trip. >> Wow, that's outstanding, Robert. We could have a whole conversation on HPE's transformation. Internet itself that was my first job in tech was at Hewlett Packard back in the day. But this has been really interesting, really getting it your vision of the customer's experience and the customer's perspective from the Office of the CTO. Great to talk to you, Robert. Thank you for sharing all that you did. This could have been a Part 2 conversation. >> Well, I'm hopeful then that we'll do Part 3 and 4 here as the months go by. So I look forward to seeing you again, Lisa. >> Deal, that's a deal. All right. >> All right. >> For Robert Christiansen, I'm Lisa Martin. You're watching theCUBE's coverage of HPE Discover 2021. (upbeat music)

>> Hello everyone, and welcome back to our wall-to-wall coverage of the Datacloud summit, this is Dave Vellante, and we're seeing the emergence of a next generation workload in the cloud, more facile access, and governed sharing of data is accelerating time to insights and action. Alright, allow me to introduce our next guest. Aimee Irwin is here, she's the vice president of strategy for Experian, and Matt Glickman is VP of customer product strategy at Snowflake, with an emphasis on financial services, folks, welcome to theCUBE, thanks so much for coming on. >> Thanks Dave, nice to be here. >> Hey so Aimee, obviously 2020's been pretty unique and crazy and challenging time for a lot of people, I don't know why, I've been checking my credit score a lot more for some reason on the app, I love the app, I had to lock it the other day, I locked my credit, somebody tried to do, and it worked, I was so happy, so thank you for that. So, we know Experian, but there's a ton of data behind what you do, I wonder if you could share kind of where you sit in the data space, and how you've seen organizations leverage data up to this point, and really if you could address some of the changes you're seeing as a result of the pandemic, that would be great. >> Sure, sure. Well, as you mentioned, Experian is best known as a credit bureau. I work in our marketing services business unit, and what we do is we really help brands leverage the power of data and technology to make the right marketing decisions, and better understand and connect with consumers. So we offer marketers products around data, identity, activation, measurement, we have a consumer-view data file that's based on offline PII and contains demographic interest, transaction data, and other attributes on about 300 million people in the US. And on the identity side we've always been known for our safe haven, or privacy-friendly matching, that allows marketers to connect their first party data to Experian or other third parties, but in today's world, with the growth in importance of digital advertising, and consumer behavior shifting to digital, Experian also is working to connect that offline data to the digital world, for a complete view of the customer. You mentioned COVID, we actually, we serve many different verticals, and what we're seeing from our clients during COVID is that there's a varying impact of the pandemic. The common theme is that those who have successfully pivoted their businesses to digital are doing much better, as we all know, COVID accelerated very strong trends to digital, both in e-commerce and in media-viewing habits. We work with a lot of retailers, retail is a tale of two cities, with big box and grocery growing, and apparel retail really struggling. We've helped our clients, leveraging our data to better understand the shifts in these consumer behaviors, and better psych-map their customers during this really challenging time. So think about, there's a group of customers that is still staying home, that is sheltered in place, there's a group of customers starting to significantly vary their consumer behavior, but is starting to venture out a little, and then there's a group of customers that's doing largely what they did before, in a somewhat modified fashion, so we're helping our clients segment those customers into groups to try and understand the right messaging and right offers for each of those groups, and we're also helping them with at-risk audiences. So that's more on the financial side, which of your customers are really struggling due to the pandemic, and how do you respond. >> That's awesome, thank you. You know, it's funny, I saw a twitter poll today asking if we measure our screen time, and I said, "oh my, no." So, Matt, let me ask you, you spent a ton of time in financial services, you really kind of cut your teeth there, and it's always been very data-oriented, you're seeing a lot of changes, tell us about how your customers are bringing it together, data, the skills, the people, obviously a big part of the equation, and applications to really put data at the center of the universe, what's new and different that these companies are getting out of the investments in data and skills? >> That's a great question, the acceleration that Aimee mentioned is real. We're seeing, particularly this year, but I think even in the past few years, the reluctance of customers to embrace the cloud is behind us, and now there's this massive acceleration to be able to go faster, and in some ways, the new entrants into this category have an advantage versus the companies that have been in this space, whether it's financial services or beyond, and in a lot of ways, they all are seeing the cloud and services like Snowflake as a way to not only catch up, but leapfrog your competitors, and really deliver a differentiated experience to your customers, to your business, internally or externally. And this past, however long this crisis has been going on, has really only accelerated that, because now there's a new demand to understand your customer better, your business better, with your traditional data sources, and also new, alternative data sources, and also being able to take a pulse. One of the things that we learned, which was an eye-opening experience, was as the crisis unfolded, one of our data partners decided to take the datasets about where the cases were happening from the Johns Hopkins, and World Health Organization, and put that on our platform, and it became a runaway hit. Thousands of our customers overnight were using this data to understand how their business was doing, versus how the crisis was unfolding in real time. And this has been a game-changer, and it's only scratching the surface of what now the world will be able to do when data is really at their fingertips, and you're not hindered by your legacy platforms. >> I wrote about that back in the early days of the pandemic when you guys did that, and talked about some of the changes that you guys enabled, and you know, you're right about cloud, in financial services cloud used to be an evil word, and now it's almost, it's become a mandate. Aimee, I wonder if you could tell us a little bit more about what your customers are having to work through in order to achieve some of these outcomes. I mean, you know, I'm interested in the starting point, I've been talking a lot, and writing a lot, and talking to practitioners about what I call the data life cycle, sometimes people call it the data pipeline, it's a complicated matter, but those customers and companies that can put data at the center and really treat that pipeline as the heart of their organization, if you will, are really succeeding. What are you seeing, and what really is the starting point, there? >> Yes, yeah, that's a good question, and as you mentioned, first party, I mean we start with first party data, right? First party data is critical to understanding consumers. And different verticals, different companies, different brands have varying levels of first party data. So a retailers going to have a lot more first party data, a financial services company, than say, an auto manufacturer. And while many marketers have that first party data, to really have a 360 view of the customer, they need third party data as well, and that's where Experian comes in, we help brands connect those disparate datasets, both first and third party data to better understand consumers, and create a single customer view, which has a number of applications. I think the last stat I heard was that there's about eight devices, on average, per person. I always joke that we're going to have these enormous, and that number's growing, we're going to have these enormous charging stations in our house, and I think we already do, because of all the different devices. And we seamlessly move from device to device, along our customer journey, and, if the brand doesn't understand who we are, it's much harder for the brand to connect with consumers and create a positive customer experience. And we cite that about 95 percent of companies, they are looking to achieve that single customer view, they recognize that they need that, and they've aligned various teams from e-commerce, to marketing, to sales, to at a minimum adjust their first party data, and then connect that data to better understand consumers. So, consumers can interact with a brand through a website, a mobile app, in-store visits, you know, by the phone, TV ads, et cetera, and a brand needs to use all of those touchpoints, often collected by different parts of the organization, and then add in that third party data to really understand the consumers. In terms of specific use cases, there's about three that come to mind. So first there's relevant advertising, and reaching the right customer, there's measurement, so being able to evaluate your advertising efforts, if you see an ad on, if I see an ad on my mobile, and then I buy by visiting a desktop website, understanding, or I get a direct mail piece, understanding that those interactions are all connected to the same person is critical for measurement. And then there's personalization, which includes improved customer experience amongst your own touchpoints with that consumer, personalized marketing communication, and then of course analytics, so those are the use cases we're seeing. >> Great, thank you Aimee. Now Matt, you can't really talk about data without talking about governance and compliance, and I remember back in 2006, when the federal rules of civil procedure went in, it was easy, the lawyers just said, "no, nobody can have access," but that's changed, and one of the things I like about what Snowflake's doing with the data cloud is it's really about democratizing access, but doing so in a way that gives people confidence that they only have access to the right data. So maybe you could talk a little bit about how you're thinking about this topic, what you're doing to help customers navigate, which has traditionally been such a really challenging problem. >> Another great question, this is where I think the major disruption is happening. And what Aimee described, being able to join together first and third party datasets, being able to do this was always a challenge, because data had to be moved around, I had to ship my first party data to the other side, and the third party data had to be shipped to me, and being able to join those datasets together was problematic at best, and now with the focus on privacy and protecting PII, this is something that has to change, and the good news is, with the data cloud, data does not have to move. Data can stay where it belongs, Experian can keep its data, Experian's customers can hold onto their data, yet the data can be joined together on this universal, global platform that we call the data cloud. On top of that, and particularly with the regulations that are coming out that are going to prevent data from being collected on either a mobile device or as cookies on web browsers, new approaches, and we're seeing this a lot in our space, both in financials and media, is to set up these data clean rooms, where both sides can give access to one another, but not have to reveal any PII to do that join. This is going to be huge, now you actually can protect your customers' and your consumers' private identities, but still accomplish that join that Aimee mentioned, to be able to relate the cause and effect of these campaigns, and really understand the signals that these datasets are trying to say about one another, again without having to move data, without having to reveal PII, we're seeing this happening now, this is the next big thing, that we're going to see explode over the months and years to come. >> I totally agree, massive changes coming in public policy in this area, and we only have a few minutes left, and I wonder if for our audience members that are looking for some advice, what's the, Aimee, what's the one thing you'd recommend they start doing differently, or consider putting in place that's going to set them up for success over the next decade? >> Yeah, that's a good question. You know, I think, I always say, first, harness all of your first party data across all touchpoints, get that first party data in one place and working together, second, connect that data with trusted third parties, and Matt suggested some ways to do that, and then third, always put the customer first, speak their language, where and when they want to be reached out to, and use the information you have to really create a better customer experience for your customers. >> Matt, what would you add to that? Bring us home, if you would. >> Applications. The idea that data, your data can now be pulled into your own business applications the same way that Netflix and Spotify are pulled into your consumer and lifestyle applications, again, without data moving, these personalized application experiences is what I encourage everyone to be thinking about from first principles. What would you do in your next app that you're going to build, if you had all your consumers, if the consumers had access to their data in the app, and not having to think about things from scratch, leverage the data cloud, leverage these service providers like Experian, and build the applications of tomorrow. >> I'm super excited when I talk to practitioners like yourselves, about the future of data, guys, thanks so much for coming on theCUBE, it was a really a pleasure having you, and I hope we can continue this conversation in the future. >> Thank you. >> Thanks. >> Alright, thank you for watching, keep it right there, we got great content, and tons of content coming at the Snowflake data cloud summit, this is Dave Vellante for theCUBE, keep it right there.

>>LA Las Vegas. It's the cube hovering AWS reinvent 2019 brought to you by Amazon web services and along with its ecosystem partners. >>Hey, welcome back everyone. This is the cubes live covers Las Vegas anus. Re-invent. I'm John furrier with Dave Alante extracting the signal from the noise sponsored by Intel and AWS. They put the stage together, two big stages. Day two, we're here day Jew, I rapid fire a devil's execs coming on. Dave McCann, cube alumni, VP of ADAS migration marketplace and control services known most for the marketplace and a lot of stuff going on. That's exciting in the marketplace. It's where all the ecosystem actions happening. Congratulations on you six. I know you're busy, you've got new stuff, but the marketplace seems to be changing the procurement and the consumption of software and solutions, whether it's SAS or images and technology, your demand on the marketplace. So great to be back, Kimberly. It's another reinvent. This is my sixth. Um, so lots going on. Marketplace has got a lot bigger in the last year. >>We're up to 260,000 customers, so not substantial growth from last year. And we're adding thousands of customers every month. Um, big headline I have to start with is marketplace has been a marketplace for software for the last seven years. And two weeks ago we launched a marketplace for data and it's a new service that we call AWS data exchange. And instead of allowing you to point, click subscribe to software, and if you're a data consumer and a bank and you're an analyst or you're a researcher and a pharma company, you actually buy data from hundreds of companies, you know, you can go into the new console, find the product and market, please go over to this console called data exchange. And you can go buy research data or you can buy healthcare data from change healthcare. You can buy news data from Thomson Reuters, you can buy consumer data from Experian. >>And we've launched 1400 products from 19 data providers and we've made it available globally. So it's a whole new class of intellectual property data sources in there as well. There's some open source public sources as well. And we're adding literally dozens of products every day. So really easy API. And the cool thing is that after you subscribe, you copy it right into your S three bucket, moves into your VPC and then you move it into your project and you can actually create a Lambda function with the next version of the data. The next day gets updated and know the data just gets updated. And the use case here is like, if I'm a retail outlet, I could buy or go and get weather data and do some things. Is that kind of the model? Exactly. Right. I mean companies all over the world by $150 billion worth of data, but it's all delivered thousands of different EPA. >>Dave, we got cube data, we put all of our advanced data out there, which might be an opportunity. But seriously, Q three 65 is our new listing on the market place. So we have a Q cloud service, little plug for the cube cube three 65 on the marketplace and we're, we're happy. But I want to ask you because one of the things that's coming up is, um, from your team in the marketplace, the industry is this notion of buying through the marketplace. The trend is increasing private offers is a hot feature that you guys have put in place. And there's some news there. Could you explain how private offers is changing the game in the marketplace? I'd love to show you, if you think about it, a lot of our customers are developers and builders and they're working on something on test and it's a pilot and you use it for a few hours or a week. >>But once a company contracts for software and if you're contracting for a lot of software, procurement, one's best price, legal one's best terms, and there's going to be in negotiation and we call that negotiation of private offer. And so that involves salespeople. And so our top software vendors like a Splunk and new Relic of trend micro, uh, Palo Alto, their sales guys, or negotiate our sales ladies and negotiating with the customer for a couple hundred thousand dollars and there's a price and terms. When are you going to pay? What clauses do you agree? How many of you buying? Where are you going to deploy? All of that's negotiated and no, we have a portal for the sailor. We've had it for a year, we've made some really good changes and the central, they arose the seller to automate that price court rate into your account and then the buyer subscribes, and this is allowing our sailors to do quotations in the hundreds of thousands, the millions and sometimes in the tens of millions on a contract rate through marketplace, you're doing millions of dollars of business with with private offers today we've seen vendors write contracts for over $10 million, Peter over three years SAS contracts. >>So we've had that program available for the last year and we'd be working on a lot of features with the help of people at Splunk and new Relic today, we've made it available for all ISBNs and marketplace. You say all the iterations get to take place in the market place, so it's all those informations. I should just speak, just make sure I get it right before private offers were invite only kind of thing. Now you're making it available to all ASVs. Correct. We've got one. As of today, we've over 1,500 ASVs in the marketplace. You're one of them. And with those 1500 vendors within our go into marketplace, there's a new button and the seller portal and it says create Piper offer and any over ISV can note create a private. So I'm going to put my little seller hat on. I have a SAS application. Look at, I don't have a big Salesforce. >>How can you guys help me? How do I, how do I get more sales? Is there a, there's the money just following my bank account. Oh, are you overstaffed to do marketing? You have to do some discussions. You know, we had a company in the UK called Matilda MAF last year on, on the cube. Medallian Staffan was 17 engineers and new salespeople and now they're like 300 people, two runs of venture and everything's through marketplace. Big booth here. Well, congratulations to those guys. We love them. And to come Mytilene again, they engage rafted with you guys. It is all the sales and go to market through AWS complete everything goes through marketplace. Okay. We've made it available to 1,500 vendors today. Okay. So changing procurement. I love that trend. You kind of modernizing the procurement process with the marketplace. What about um, resellers? What's the update there? >>So the big update there is, you know, for the first six years of marketplace we couldn't handle the resaler. We didn't conceive of the VAR or the consulting partner and we got a lot of feedback that we had to do work. And so we've taken private offers and we've designed consulting partner, private offers and no, we've saved up over a hundred top consulting partner resellers, the likes of an OCT of an Ashi, a Rackspace in Europe computer center and Softcat and they were working with all of the world's top resellers and know if you are a Splunk or trend micro, you can authorize computer center to offer private prices to their customers and you can actually authorize a wholesale price from Splunk directly to computer and get paid for. Well, they could actually set the price. Mark it up. I got to ask you, Dave, what's your vision for marketplace? >>Because you're doing a great job. It seems like you're paddling as fast as you can constantly improving the service. I know you've got a big to do list, you want to make it easy or make it faster, all that good stuff, but what's the vision? Where do you see marketplace evolving? You know, Jeff Bezos says it's only day one. We're seven years old. We've barely scratched the surface. Global software is 450 billion growing 8% data is 150 billion growing at 3% you've got a $600 billion industry. Marketplace has not touched a tiny percentage. We want all of our customers to be able to find, discover, provision, and run all of their software and their data out of marketplace and it's gonna take us another 10 years and you get a lot of teen. How big is the team? We never publish JFK K but just let's say the Andy Jassy continues to invest in the business and as we add engineers and we add business people and development people, you know we work well with our partners. >>We cool market. Yeah, we grew up well, as Andy always says, you know, and you always say this, the customer needs come first. That's kind of a vetting process. Then working backwards documents, we know all about that history. What is the number one customer need that you're hearing, that you're addressing, that you see coming up around the corner, you're constantly working on and new potentially new requests that are coming in that are relevant to your business. There's two or three big customer needs. The number one is governance. So while engineers are going fast, innovating, legal, finance and procurement need to be confident that the contracts are being written well and is the spend under control. And so we're doing a lot of work around tagging or the resources so that it's tagged to the right project. Did you overspend on the project? And then on the contracting inside we launched this thing called enterprise contract and we're continuing to work with customers. >>We just integrated into the leading procurement system called ACP a Reebok and we launched that last week. And so we know have a procurement workflow that says procurement's happy it finances happy legal needs to be happy because the engineers want to go quick, but we can't leave the it finance legal professionals behind because they protect the risks for the kinda, the contracts too are all there. So you're modernizing procurement. We are transforming the supply chain for data and for software, you know big. You know I'm a big fan of what you do and I know you got a lot of hard work, a lot of demand, there's a lot of money to be made there, water customers to make happy and you know we've got great customers that BP or shell or Coca Cola, Coke industries that are using marketplace on a regular basis and we have customers now with over a foes and subscriptions from over 50 vendors and that's a single customer. >>Dave, thank you so much for coming on. I know you're super busy and making the time for wrestling the cube means a lot. You've been with us the entire journey for the Ravens, our seventh reinvent. You've been a great one. I missed one but usually patients man it's just you. You saw it working backwards and it's happening. It's working well and you know we learn from our customers and I'm having a dinner tonight with 40 more and I'm sure they'll hit us with more requirements. I'll check my email for the invite. I'm sure it's in there somewhere. Dave McKenna inside the cube. Good friend of the cube, hardworking, billable in the next generation, the next gen marketplace. Check it out. Of course, the cube three 65 our new offering is up there as of Monday. It's kind of a soft launch, but we're telling you now, I'm John Freud. Dave Volante. Thanks for watching back with more. Thanks and have a short break.

>> From Cambridge, Massachusetts, it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019, brought to you by SiliconANGLE Media. >> Welcome back to Cambridge, Massachusetts everybody, you're watching The Cube, the leader in tech coverage. My name is Dave Vellante here with my cohost Paul Gillin. This is day one of our two day coverage of the MIT CDOIQ conferences. CDO, Chief Data Officer, IQ, information quality. Colin Mahoney is here, he's a good friend and long time CUBE alum. I haven't seen you in awhile, >> I know >> But thank you so much for taking some time, you're like a special guest here >> Thank you, yeah it's great to be here, thank you. >> Yeah, so, this is not, you know, something that you would normally attend. I caught up with you, invited you in. This conference has started as, like back office governance, information quality, kind of wonky stuff, hidden. And then when the big data meme took off, kind of around the time we met. The Chief Data Officer role emerged, the whole Hadoop thing exploded, and then this conference kind of got bigger and bigger and bigger. Still intimate, but very high level, very senior. It's kind of come full circle as we've been saying, you know, information quality still matters. You have been in this data business forever, so I wanted to invite you in just to get your perspectives, we'll talk about what's new with what's going on in your company, but let's go back a little bit. When we first met and even before, you saw it coming, you kind of invested your whole career into data. So, take us back 10 years, I mean it was so different, remember it was Batch, it was Hadoop, but it was cool. There was a lot of cool >> It's still cool. (laughs) projects going on, and it's still cool. But, take a look back. >> Yeah, so it's changed a lot, look, I got into it a while ago, I've always loved data, I had no idea, the explosion and the three V's of data that we've seen over the last decade. But, data's really important, and it's just going to get more and more important. But as I look back I think what's really changed, and even if you just go back a decade I mean, there's an insatiable appetite for data. And that is not slowing down, it hasn't slowed down at all, and I think everybody wants that perfect solution that they can ask any question and get an immediate answers to. We went through the Hadoop boom, I'd argue that we're going through the Hadoop bust, but what people actually want is still the same. You know, they want real answers, accurate answers, they want them quickly, and they want it against all their information and all their data. And I think that Hadoop evolved a lot as well, you know, it started as one thing 10 years ago, with MapReduce and I think in the end what it's really been about is disrupting the storage market. But if you really look at what's disrupting storage right now, public clouds, S3, right? That's the new data league. So there's always a lot of hype cycles, everybody talks about you know, now it's Cloud, everything, for maybe the last 10 years it was a lot of Hadoop, but at the end of the day I think what people want to do with data is still very much the same. And a lot of companies are still struggling with it, hence the role for Chief Data Officers to really figure out how do I monetize data on the one hand and how to I protect that asset on the other hand. >> Well so, and the cool this is, so this conference is not a tech conference, really. And we love tech, we love talking about this, this is why I love having you on. We kind of have a little Vertica thread that I've created here, so Colin essentially, is the current CEO of Vertica, I know that's not your title, you're GM and Senior Vice President, but you're running Vertica. So, Michael Stonebreaker's coming on tomorrow, >> Yeah, excellent. >> Chris Lynch is coming on tomorrow, >> Oh, great, yeah. >> we've got Andy Palmer >> Awesome, yeah. >> coming up as well. >> Pretty cool. (laughs) >> So we have this connection, why is that important? It's because, you know, Vertica is a very cool company and is all about data, and it was all about disrupting, sort of the traditional relational database. It's kind of doing more with data, and if you go back to the roots of Vertica, it was like how do you do things faster? How do you really take advantage of data to really drive new business? And that's kind of what it's all about. And the tech behind it is really cool, we did your conference for many, many years. >> It's coming back by the way. >> Is it? >> Yeah, this March, so March 30th. >> Oh, wow, mark that down. >> At Boston, at the new Encore Hotel. >> Well we better have theCUBE there, bro. (laughs) >> Yeah, that's great. And yeah, you've done that conference >> Yep. >> haven't you before? So very cool customers, kind of leading edge, so I want to get to some of that, but let's talk the disruption for a minute. So you guys started with the whole architecture, MPP and so forth. And you talked about Cloud, Cloud really disrupted Hadoop. What are some of the other technology disruptions that you're seeing in the market space? >> I think, I mean, you know, it's hard not to talk about AI machine learning, and what one means versus the other, who knows right? But I think one thing that is definitely happening is people are leveraging the volumes of data and they're trying to use all the processing power and storage power that we have to do things that humans either are too expensive to do or simply can't do at the same speed and scale. And so, I think we're going through a renaissance where a lot more is being automated, certainly on the Vertica roadmap, and our path has always been initially to get the data in and then we want the platform to do a lot more for our customers, lots more analytics, lots more machine-learning in the platform. So that's definitely been a lot of the buzz around, but what's really funny is when you talk to a lot of customers they're still struggling with just some basic stuff. Forget about the predictive thing, first you've got to get to what happened in the past. Let's give accurate reporting on what's actually happening. The other big thing I think as a disruption is, I think IOT, for all the hype that it's getting it's very real. And every device is kicking off lots of information, the feedback loop of AB testing or quality testing for predictive maintenance, it's happening almost instantly. And so you're getting massive amounts of new data coming in, it's all this machine sensor type data, you got to figure out what it means really quick, and then you actually have to do something and act on it within seconds. And that's a whole new area for so many people. It's not their traditional enterprise data network warehouse and you know, back to you comment on Stonebreaker, he got a lot of this right from the beginning, you know, and I think he looked at the architectures, he took a lot of the best in class designs, we didn't necessarily invent everything, but we put a lot of that together. And then I think the other you've got to do is constantly re-invent your platform. We came out with our Eon Mode to run cloud native, we just got rated the best cloud data warehouse from a net promoter score rating perspective, so, but we got to keep going you know, we got to keep re-inventing ourselves, but leverage everything that we've done in the past as well. >> So one of the things that you said, which is kind of relevant for here, Paul, is you're still seeing a real data quality issue that customers are wrestling with, and that's a big theme here, isn't it? >> Absolutely, and the, what goes around comes around, as Dave said earlier, we're still talking about information quality 13 years after this conference began. Have the tools to improve quality improved all that much? >> I think the tools have improved, I think that's another area where machine learning, if you look at Tamr, and I know you're going to have Andy here tomorrow, they're leveraging a lot of the augmented things you can do with the processing to make it better. But I think one thing that makes the problem worse now, is it's gotten really easy to pour data in. It's gotten really easy to store data without having to have the right structure, the right quality, you know, 10 years ago, 20 years ago, everything was perfect before it got into the platform. Right, everything was, there was quality, everything was there. What's been happening over the last decade is you're pumping data into these systems, nobody knows if it's redundant data, nobody knows if the quality's any good, and the amount of data is massive. >> And it's cheap to store >> Very cheap to store. >> So people keep pumping it in. >> But I think that creates a lot of issues when it comes to data quality. So, I do think the technology's gotten better, I think there's a lot of companies that are doing a great job with it, but I think the challenge has definitely upped. >> So, go ahead. >> I'm sorry. You mentioned earlier that we're seeing the death of Hadoop, but I'd like you to elaborate on that becuase (Dave laughs) Hadoop actually came up this morning in the keynote, it's part of what GlaxoSmithKline did. Came up in a conversation I had with the CEO of Experian last week, I mean, it's still out there, why do you think it's in decline? >> I think, I mean first of all if you look at the Hadoop vendors that are out there, they've all been struggling. I mean some of them are shutting down, two of them have merged and they've got killed lately. I think there are some very successful implementations of Hadoop. I think Hadoop as a storage environment is wonderful, I think you can process a lot of data on Hadoop, but the problem with Hadoop is it became the panacea that was going to solve all things data. It was going to be the database, it was going to be the data warehouse, it was going to do everything. >> That's usually the kiss of death, isn't it? >> It's the kiss of death. And it, you know, the killer app on Hadoop, ironically, became SQL. I mean, SQL's the killer app on Hadoop. If you want to SQL engine, you don't need Hadoop. But what we did was, in the beginning Mike sort of made fun of it, Stonebreaker, and joked a lot about he's heard of MapReduce, it's called Group By, (Dave laughs) and that created a lot of tension between the early Vertica and Hadoop. I think, in the end, we embraced it. We sit next to Hadoop, we sit on top of Hadoop, we sit behind it, we sit in front of it, it's there. But I think what the reality check of the industry has been, certainly by the business folks in these companies is it has not fulfilled all the promises, it has not fulfilled a fraction on the promises that they bet on, and so they need to figure those things out. So I don't think it's going to go away completely, but I think its best success has been disrupting the storage market, and I think there's some much larger disruptions of technologies that frankly are better than HTFS to do that. >> And the Cloud was a gamechanger >> And a lot of them are in the cloud. >> Which is ironic, 'cause you know, cloud era, (Colin laughs) they didn't really have a cloud strategy, neither did Hortonworks, neither did MapR and, it just so happened Amazon had one, Google had one, and Microsoft has one, so, it's just convenient to-- >> Well, how is that affecting your business? We've seen this massive migration to the cloud (mumbles) >> It's actually been great for us, so one of the things about Vertica is we run everywhere, and we made a decision a while ago, we had our own data warehouse as a service offering. It might have been ahead of its time, never really took off, what we did instead is we pivoted and we say "you know what? "We're going to invest in that experience "so it's a SaaS-like experience, "but we're going to let our customers "have full control over the cloud. "And if they want to go to Amazon they can, "if they want to go to Google they can, "if they want to go to Azure they can." And we really invested in that and that experience. We're up on the Amazon marketplace, we have lots of customers running up on Amazon Cloud as well as Google and Azure now, and then about two years ago we went down and did this endeavor to completely re-architect our product so that we could separate compute and storage so that our customers could actually take advantage of the cloud economics as well. That's been huge for us, >> So you scale independent-- >> Scale independently, cloud native, add compute, take away compute, and for our existing customers, they're loving the hybrid aspect, they love that they can still run on Premise, they love that they can run up on a public cloud, they love that they can run in both places. So we will continue to invest a lot in that. And it is really, really important, and frankly, I think cloud has helped Vertica a lot, because being able to provision hardware quickly, being able to tie in to these public clouds, into our customers' accounts, give them control, has been great and we're going to continue on that path. >> Because Vertica's an ISV, I mean you're a software company. >> We're a software company. >> I know you were a part of HP for a while, and HP wanted to mash that in and run it on it's hardware, but software runs great in the cloud. And then to you it's another hardware platform. >> It's another hardware platform, exactly. >> So give us the update on Micro Focus, Micro Focus acquired Vertica as part of the HPE software business, how many years ago now? Two years ago? >> Less than two years ago. >> Okay, so how's that going, >> It's going great. >> Give us the update there. >> Yeah, so first of all it is great, HPE and HP were wonderful to Vertica, but it's great being part of a software company. Micro Focus is a software company. And more than just a software company it's a company that has a lot of experience bridging the old and the new. Leveraging all of the investments that you've made but also thinking about cloud and all these other things that are coming down the pike. I think for Vertica it's been really great because, as you've seen Vertica has gotten its identity back again. And that's something that Micro Focus is very good at. You can look at what Micro Focus did with SUSE, the Linux company, which actually you know, now just recently spun out of Micro Focus but, letting organizations like Vertica that have this culture, have this product, have this passion, really focus on our market and our customers and doing the right thing by them has been just really great for us and operating as a software company. The other nice thing is that we do integrate with a lot of other products, some of which came from the HPE side, some of which came from Micro Focus, security products is an example. The other really nice thing is we've been doing this insource thing at Micro Focus where we open up our source code to some of the other teams in Micro Focus and they've been contributing now in amazing ways to the product. In ways that we would just never be able to scale, but with 4,000 engineers strong in Micro Focus, we've got a much larger development organization that can actually contribute to the things that Vertica needs to do. And as we go into the cloud and as we do a lot more operational aspects, the experience that these teams have has been incredible, and security's another great example there. So overall it's been great, we've had four different owners of Vertica, our job is to continue what we do on the innovation side in the culture, but so far Micro Focus has been terrific. >> Well, I'd like to say, you're kind of getting that mojo back, because you guys as an independent company were doing your own thing, and then you did for a while inside of HP, >> We did. >> And that obviously changed, 'cause they wanted more integration, but, and Micro Focus, they know what they're doing, they know how to do acquisitions, they've been very successful. >> It's a very well run company, operationally. >> The SUSE piece was really interesting, spinning that out, because now RHEL is part of IBM, so now you've got SUSE as the lone independent. >> Yeah. >> Yeah. >> But I want to ask you, go back to a technology question, is NoSQL the next Hadoop? Are these databases, it seems to be that the hot fad now is NoSQL, it can do anything. Is the promise overblown? >> I think, I mean NoSQL has been out almost as long as Hadoop, and I, we always say not only SQL, right? Mike's said this from day one, best tool for the job. Nothing is going to do every job well, so I think that there are, whether it's key value stores or other types of NoSQL engines, document DB's, now you have some of these DB's that are running on different chips, >> Graph, yeah. >> there's always, yeah, graph DBs, there's always going to be specialty things. I think one of the things about our analytic platform is we can do, time series is a great example. Vertica's a great time series database. We can compete with specialized time series databases. But we also offer a lot of, the other things that you can do with Vertica that you wouldn't be able to do on a database like that. So, I always think there's going to be specialty products, I also think some of these can do a lot more workloads than you might think, but I don't see as much around the NoSQL movement as say I did a few years ago. >> But so, and you mentioned the cloud before as kind of, your position on it I think is a tailwind, not to put words in your mouth, >> Yeah, yeah, it's a great tailwind. >> You're in the Amazon marketplace, I mean they have products that are competitive, right? >> They do, they do. >> But, so how are you differentiating there? >> I think the way we differentiate, whether it's Redshift from Amazon, or BigQuery from Google, or even what Azure DB does is, first of all, Vertica, I think from, feature functionality and performance standpoint is ahead. Number one, I think the second thing, and we hear this from a lot of customers, especially at the C-level is they don't want to be locked into these full stacks of the clouds. Having the ability to take a product and run it across multiple clouds is a big thing, because the stack lock-in now, the full stack lock-in of these clouds is scary. It's really easy to develop in their ecosystems but you get very locked into them, and I think a lot of people are concerned about that. So that works really well for Vertica, but I think at the end of the day it's just, it's the robustness of the product, we continue to innovate, when you look at separating compute and storage, believe it or not, a lot of these cloud-native databases don't do that. And so we can actually leverage a lot of the cloud hardware better than the native cloud databases do themselves. So, like I said, we have to keep going, those guys aren't going to stop, and we actually have great relationships with those companies, we work really well with the clouds, they seem to care just as much about their cloud ecosystem as their own database products, and so I think that's going to continue as well. >> Well, Colin, congratulations on all the success >> Yeah, thank you, yeah. >> It's awesome to see you again and really appreciate you coming to >> Oh thank you, it's great, I appreciate the invite, >> MIT. >> it's great to be here. >> All right, keep it right there everybody, Paul and I will be back with our next guest from MIT, you're watching theCUBE. (electronic jingle)

>> From Cambridge, Massachusetts, it's theCUBE, covering MIT Chief Data Officer and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. >> Hi, everybody, welcome back to Cambridge, Massachusetts. This is theCUBE, the leader in tech coverage. I'm Dave Vellante with my cohost, Paul Gillin, and we're here covering the MIT Chief Data Officer Information Quality Conference, #MITCDOIQ. Lisa Ehrlinger is here, she's the Senior Researcher at the Johannes Kepler University in Linz, Austria, and the Software Competence Center in Hagenberg. Lisa, thanks for coming in theCUBE, great to see you. >> Thanks for having me, it's great to be here. >> You're welcome. So Friday you're going to lay out the results of the study, and it's a study of Data Quality Tools. Kind of the long tail of tools, some of those ones that may not have made the Gartner Magic Quadrant and maybe other studies, but talk about the study and why it was initiated. >> Okay, so the main motivation for this study was actually a very practical one, because we have many company projects with companies from different domains, like steel industry, financial sector, and also focus on automotive industry at our department at Johannes Kepler University in Linz. We have experience with these companies for more than 20 years, actually, in this department, and what reoccurred was the fact that we spent the majority of time in such big data projects on data quality measurement and improvement tasks. So at some point we thought, okay, what possibilities are there to automate these tasks and what tools are out there on the market to automate these data quality tasks. So this was actually the motivation why we thought, okay, we'll look at those tools. Also, companies ask us, "Do you have any suggestions? "Which tool performs best in this-and-this domain?" And I think this study answers some questions that have not been answered so far in this particular detail, in these details. For example, Gartner Magic Quadrant of Data Quality Tools, it's pretty interesting but it's very high-level and focusing on some global windows, but it does not look on the specific measurement functionalities. >> Yeah, you have to have some certain number of whatever, customers or revenue to get into the Magic Quadrant. So there's a long tail that they don't cover. But talk a little bit more about the methodology, was it sort of you got hands-on or was it more just kind of investigating what the capabilities of the tools were, talking to customers? How did you come to the conclusions? >> We actually approached this from a very scientific side. We conducted a systematic search, which tools are out there on the market, not only industrial tools, but also open-sourced tools were included. And I think this gives a really nice digest of the market from different perspectives, because we also include some tools that have not been investigated by Gartner, for example, like more BTQ, Data Quality, or Apache Griffin, which has really nice monitoring capabilities, but lacks some other features from these comprehensive tools, of course. >> So was the goal of the methodology largely to capture a feature function analysis of being able to compare that in terms of binary, did it have it or not, how robust is it? And try to develop a common taxonomy across all these tools, is that what you did? >> So we came up with a very detailed requirements catalog, which is divided into three fields, like the focuses on data profiling to get a first insight into data quality. The second is data quality management in terms of dimensions, metrics, and rules. And the third part is dedicated to data quality monitoring over time, and for all those three categories, we came up with different case studies on a database, on a test database. And so we conducted, we looked, okay, does this tool, yes, support this feature, no, or partially? And when partially, to which extent? So I think, especially on the partial assessment, we got a lot into detail in our survey, which is available on Archive online already. So the preliminary results are already online. >> How do you find it? Where is it available? >> On Archive. >> Archive? >> Yes. >> What's the URL, sorry. Archive.com, or .org, or-- >> Archive.org, yeah. >> Archive.org. >> But actually there is a ID I have not with me currently, but I can send you afterwards, yeah. >> Yeah, maybe you can post that with the show notes. >> We can post it afterwards. >> I was amazed, you tested 667 tools. Now, I would've expected that there would be 30 or 40. Where are all of these, what do all of these long tail tools do? Are they specialized by industry or by function? >> Oh, sorry, I think we got some confusion here, because we identified 667 tools out there on the market, but we narrowed this down. Because, as you said, it's quite impossible to observe all those tools. >> But the question still stands, what is the difference, what are these very small, niche tools? What do they do? >> So most of them are domain-specific, and I think this really highlights also these very basic early definition about data quality, of like data qualities defined as fitness for use, and we can pretty much see it here that we excluded the majority of these tools just because they assess some specific kind of data, and we just really wanted to find tools that are generally applicable for different kinds of data, for structured data, unstructured data, and so on. And most of these tools, okay, someone came up with, we want to assess the quality of our, I don't know, like geological data or something like that, yeah. >> To what extent did you consider other sort of non-technical factors? Did you do that at all? I mean, was there pricing or complexity of downloading or, you know, is there a free version available? Did you ignore those and just focus on the feature function, or did those play a role? >> So basically the focus was on the feature function, but of course we had to contact the customer support. Especially with the commercial tools, we had to ask them to provide us with some trial licenses, and there we perceived different feedback from those companies, and I think the best comprehensive study here is definitely Gartner Magic Quadrant for Data Quality Tools, because they give a broad assessment here, but what we also highlight in our study are companies that have a very open support and they are very willing to support you. For example, Informatica Data Quality, we perceived a really close interaction with them in terms of support, trial licenses, and also like specific functionality. Also Experian, our contact from Experian from France was really helpful here. And other companies, like IBM, they focus on big vendors, and here, it was not able to assess these tools, for example, yeah. >> Okay, but the other differences of the Magic Quadrant is you guys actually used the tools, played with them, experienced firsthand the customer experience. >> Exactly, yeah. >> Did you talk to customers as well, or, because you were the customer, you had that experience. >> Yes, I were the customer, but I was also happy to attend some data quality event in Vienna, and there I met some other customers who had experience with single tools. Not of course this wide range we observed, but it was interesting to get feedback on single tools and verify our results, and it matched pretty good. >> How large was the team that ran the study? >> Five people. >> Five people, and how long did it take you from start to finish? >> Actually, we performed it for one year, roughly. The assessment. And I think it's a pretty long time, especially when you see how quick the market responds, especially in the open source field. But nevertheless, you need to make some cut, and I think it's a very recent study now, and there is also the idea to publish it now, the preliminary results, and we are happy with that. >> Were there any surprises in the results? >> I think the main results, or one of the surprises was that we think that there is definitely more potential for automation, but not only for automation. I really enjoyed the keynote this morning that we need more automation, but at the same time, we think that there is also the demand for more declaration. We observed some tools that say, yeah, we apply machine learning, and then you look into their documentation and find no information, which algorithm, which parameters, which thresholds. So I think this is definitely, especially if you want to assess the data quality, you really need to know what algorithm and how it's attuned and give the user, which in most case will be a technical person with technical background, like some chief data officer. And he or she really needs to have the possibility to tune these algorithms to get reliable results and to know what's going on and why, which records are selected, for example. >> So now what? You're presenting the results, right? You're obviously here at this conference and other conferences, and so it's been what, a year, right? >> Yes. >> And so what's the next wave? What's next for you? >> The next wave, we're currently working on a project which is called some Knowledge Graph for Data Quality Assessment, which should tackle two problems in ones. The first is to come up with a semantic representation of your data landscape in your company, but not only the data landscape itself in terms of gathering meta data, but also to automatically improve or annotate this data schema with data profiles. And I think what we've seen in the tools, we have a lot of capabilities for data profiling, but this is usually left to the user ad hoc, and here, we store it centrally and allow the user to continuously verify newly incoming data if this adheres to this standard data profile. And I think this is definitely one step into the way into more automation, and also I think it's the most... The best thing here with this approach would be to overcome this very arduous way of coming up with all the single rules within a team, but present the data profile to a group of data, within your data quality project to those peoples involved in the projects, and then they can verify the project and only update it and refine it, but they have some automated basis that is presented to them. >> Oh, great, same team or new team? >> Same team, yeah. >> Oh, great. >> We're continuing with it. >> Well, Lisa, thanks so much for coming to theCUBE and sharing the results of your study. Good luck with your talk on Friday. >> Thank you very much, thank you. >> All right, and thank you for watching. Keep it right there, everybody. We'll be back with our next guest right after this short break. From MIT CDOIQ, you're watching theCUBE. (upbeat music)

>> From Cambridge, Massachusetts, it's The Cube! Covering MIT Chief Data Officer and Information Qualities Symposium 2019. Brought to you by SiliconANGLE Media. >> Welcome to Cambridge, Massachusetts everybody. You're watching The Cube, the leader in live tech coverage. My name is Dave Vellante and I'm here with my cohost Paul Gillin. And we're covering the 13th annual MIT CDOIQ conference. The Cube first started here in 2013 when the whole industry Paul, this segment of the industry was kind of moving out of the ashes of the compliance world and the data quality world and kind of that back office role, and it had this tailwind of the so called big data movement behind it. And the Chief Data Officer was emerging very strongly within as we've talked about many times in theCube, within highly regulated industries like financial services and government and healthcare and now we're seeing data professionals from all industries join this symposium at MIT as I say 13th year, and we're now seeing a lot of discussion about not only the role of the Chief Data Officer, but some of what we heard this morning from Mark Ramsey some of the failures along the way of all these north star data initiatives, and kind of what to do about it. So this conference brings together several hundred practitioners and we're going to be here for two days just unpacking all the discussions the major trends that touch on data. The data revolution, whether it's digital transformation, privacy, security, blockchain and the like. Now Paul, you've been involved in this conference for a number of years, and you've seen it evolve. You've seen that chief data officer role both emerge from the back office into a c-level executive role, and now spanning a very wide scope of responsibilities. Your thoughts? >> It's been like being part of a soap opera for the last eight years that I've been part of this conference because as you said Dave, we've gone through all of these transitions. In the early days this conference actually started as an information qualities symposium. It has evolved to become about chief data officer and really about the data as an asset to the organization. And I thought that the presentation we saw this morning, Mark Ramsey's talk, we're going to have him on later, very interesting about what they did at GlaxoSmithKline to get their arms around all of the data within that organization. Now a project like that would've unthinkable five years ago, but we've seen all of these new technologies come on board, essentially they've created a massive search engine for all of their data. We're seeing organizations beginning to get their arms around this massive problem. And along the way I say it's a soap opera because along the way we've seen failure after failure, we heard from Mark this morning that data governance is a failure too. That was news to me! All of these promising initiatives that have started and fallen flat or failed to live up to their potential, the chief data officer role has emerged out of that to finally try to get beyond these failures and really get their arms around that organizational data and it's a huge project, and it's something that we're beginning to see some organization succeed at. >> So let's talk a little bit about the role. So the chief data officer in many ways has taken a lot of the heat off the chief information officer, right? It used to be CIO stood for career is over. Well, when you throw all the data problems at an individual c-level executive, that really is a huge challenge. And so, with the cloud it's created opportunities for CIOs to actually unburden themselves of some of the crapplications and actually focus on some of the mission critical stuff that they've always been really strong at and focus their budgets there. But the chief data officer has had somewhat of an unclear scope. Different organizations have different roles and responsibilities. And there's overlap with the chief digital officer. There's a lot of emphasis on monetization whether that's increasing revenue or cutting costs. And as we heard today from the keynote speaker Mark Ramsey, a lot of the data initiatives have failed. So what's your take on that role and its viability and its longterm staying power? >> I think it's coming together. I think last year we saw the first evidence of that. I talked to a number of CDOs last year as well as some of the analysts who were at this conference, and there was pretty good clarity beginning to emerge about what they chief data officer role stood for. I think a lot of what has driven this is this digital transformation, the hot buzz word of 2019. The foundation of digital transformation is a data oriented culture. It's structuring the entire organization around data, and when you get to that point when an organization is ready to do that, then the role of the CDO I think becomes crystal clear. It's not so much just an extract transform load discipline. It's not just technology, it's not just governance. It really is getting that data, pulling that data together and putting it at the center of the organization. That's the value that the CDO can provide, I think organizations are coming around to that. >> Yeah and so we've seen over the last 10 years the decrease, the rapid decrease in cost, the cost of storage. Microprocessor performance we've talked about endlessly. And now you've got the machine intelligence piece layering in. In the early days Hadoop was the hot tech, and interesting now nobody talks even about Hadoop. Rarely. >> Yet it was discussed this morning. >> It was mentioned today. It is a fundamental component of infrastructures. >> Yeah. >> But what it did is it dramatically lowered the cost of storing data, and allowing people to leave data in place. The old adage of ship a five megabytes of code to a petabyte of data versus the reverse. Although we did hear today from Mark Ramsey that they copied all the data into a centralized location so I got some questions on that. But the point I want to make is that was really early days. We're now entered an era and it's underscored by if you look at the top five companies in terms of market cap in the US stock market, obviously Microsoft is now over a trillion. Microsoft, Apple, Amazon, Google and Facebook. Top five. They're data companies, their assets are all data driven. They've surpassed the banks, the energy companies, of course any manufacturing automobile companies, et cetera, et cetera. So they're data companies, and they're wrestling with big issues around security. You can't help but open the paper and see issues on security. Yesterday was the big Capital One. The Equifax issue was resolved in terms of the settlement this week, et cetera, et cetera. Facebook struggling mightily with whether or not how to deal fake news, how to deal with deep fakes. Recently it shut down likes for many Instagram accounts in some countries because they're trying to protect young people who are addicted to this. Well, they need to shut down likes for business accounts. So what kids are doing is they're moving over to the business Instagram accounts. Well when that happens, it exposes their emails automatically so they've all kinds of privacy landmines and people don't know how to deal with them. So this data explosion, while there's a lot of energy and excitement around it, brings together a lot of really sticky issues. And that falls right in the lap of the chief data officer, doesn't it? >> We're in uncharted territory and all of the examples you used are problems that we couldn't have foreseen, those companies couldn't have foreseen. A problem may be created but then the person who suffers from that problem changes their behavior and it creates new problems as you point out with kids shifting where they're going to communicate with each other. So these are all uncharted waters and I think it's got to be scary if you're a company that does have large amounts of consumer data in particular, consumer packaged goods companies for example, you're looking at what's happening to these big companies and these data breaches and you know that you're sitting on a lot of customer data yourself, and that's scary. So we may see some backlash to this from companies that were all bought in to the idea of the 360 degree customer view and having these robust data sources about each one of your customers. Turns out now that that's kind of a dangerous place to be. But to your point, these are data companies, the companies that business people look up to now, that they emulate, are companies that have data at their core. And that's not going to change, and that's certainly got to be good for the role of the CDO. >> I've often said that the enterprise data warehouse failed to live up to its expectations and its promises. And Sarbanes-Oxley basically saved EDW because reporting became a critical component post Enron. Mark Ramsey talked today about EDW failing, master data management failing as kind of a mapping and masking exercise. The enterprise data model which was a top down push for a sort of distraction layer, that failed. You had all these failures and so we turned to governance. That failed. And so you've had this series of issues. >> Let me just point out, what do all those have in common? They're all top down. >> Right. >> All top down initiatives. And what Glaxo did is turn that model on its head and left the data where it was. Went and discovered it and figured it out without actually messing with the data. That may be the difference that changes the game. >> Yeah and it's prescription was basically taking a tactical approach to that problem, start small, get quick hits. And then I think they selected a workload that was appropriate for solving this problem which was clinical trials. And I have some questions for him. And of the big things that struck me is the edge. So as you see a new emerging data coming out of the edge, how are organizations going to deal with that? Because I think a lot of what he was talking about was a lot of legacy on-prem systems and data. Think about JEDI, a story we've been following on SiliconANGLE the joint enterprise defense infrastructure. This is all about the DOD basically becoming cloud enabled. So getting data out into the field during wartime fast. We're talking about satellite data, you're talking about telemetry, analytics, AI data. A lot of distributed data at the edge bringing new challenges to how organizations are going to deal with data problems. It's a whole new realm of complexity. >> And you talk about security issues. When you have a lot of data at the edge and you're sending data to the edge, you're bringing it back in from the edge, every device in the middle is from the smart thermostat. at the edge all the way up to the cloud is a potential failure point, a potential vulnerability point. These are uncharted waters, right? We haven't had to do this on a large scale. Organizations like the DOD are going to be the ones that are going to be the leaders in figuring this out because they are so aggressive. They have such an aggressive infrastructure and place. >> The other question I had, striking question listening to Mark Ramsey this morning. Again Mark Ramsey was former data God at GSK, GlaxoSmithKline now a consultant. We're going to hear from a number of folks like him and chief data officers. But he basically kind of poopooed, he used the example of build it and they will come. You know the Kevin Costner movie Field of Dreams. Don't go after the field of dreams. So my question is, and I wonder if you can weigh in on this is, everywhere we go we hear about digital transformation. They have these big digital transformation projects, they generally are top down. Every CEO wants to get digital right. Is that the wrong approach? I want to ask Mark Ramsey that. Are they doing field of dreams type stuff? Is it going to be yet another failure of traditional legacy systems to try to compete with cloud native and born in data era companies? >> Well he mentioned this morning that the research is already showing that digital transformation most initiatives are failing. Largely because of cultural reasons not technical reasons, and I think Ramsey underscored that point this morning. It's interesting that he led off by mentioning business process reengineering which you remember was a big fad in the 1990s, companies threw billions of dollars at trying to reinvent themselves and most of them failed. Is digital transformation headed down the same path? I think so. And not because the technology isn't there, it's because creating a culture where you can break down these silos and you can get everyone oriented around a single view of the organizations data. The bigger the organization the less likely that is to happen. So what does that mean for the CDO? Well, chief information officer at one point we said the CIO stood for career is over. I wonder if there'll be a corresponding analogy for the CDOs at some of these big organizations when it becomes obvious that pulling all that data together is just not feasible. It sounds like they've done something remarkable at GSK, maybe we'll learn from that example. But not all organizations have the executive support, which was critical to what they did, or just the organizational will to organize themselves around that central data storm. >> And I also said before I think the CDO is taking a lot of heat off the CIO and again my inference was the GSK use case and workload was actually quite narrow in clinical trials and was well suited to success. So my takeaway in this, if I were CDO what I would be doing is trying to figure out okay how does data contribute to the monetization of my organization? Maybe not directly selling the data, but what data do I have that's valuable and how can I monetize that in terms of either saving money, supply chain, logistics, et cetera, et cetera, or making money? Some kind of new revenue opportunity. And I would super glue myself for the line of business executive and go after a small hit. You're talking about digital transformations being top down and largely failing. Shadow digital transformations is maybe the answer to that. Aligning with a line of business, focusing on a very narrow use case, and building successes up that way using data as the ingredient to drive value. >> And big ideas. I recently wrote about Experian which launched a service last called Boost that enables the consumers to actually impact their own credit scores by giving Experian access to their bank accounts to see that they are at better credit risk than maybe portrayed in the credit store. And something like 600,000 people signed up in the first six months of this service. That's an example I think of using inspiration, creating new ideas about how data can be applied And in the process by the way, Experian gains data that they can use in other context to better understand their consumer customers. >> So digital meets data. Data is not the new oil, data is more valuable than oil because you can use it multiple times. The same data can be put in your car or in your house. >> Wish we could do that with the oil. >> You can't do that with oil. So what does that mean? That means it creates more data, more complexity, more security risks, more privacy risks, more compliance complexity, but yet at the same time more opportunities. So we'll be breaking that down all day, Paul and myself. Two days of coverage here at MIT, hashtag MITCDOIQ. You're watching The Cube, we'll be right back right after this short break. (upbeat music)

>> Live from Las Vegas. It's theCUBE. Covering Informatica World 2019. Brought to you by Informatica. >> Welcome back everyone to theCUBE's live coverage of Informatica World 2019. I'm your host, Rebecca Knight. We are joined by Sanjeev Vhora. He is the group technology officer and global data business lead at Accenture. Thank you so much for coming on theCUBE. >> Thanks. Thanks for having me here. >> We're hearing so much about AI lead data intelligence, and the other buzz word of course, that we hear so much of, is digital transformation. I'd love to hear your thoughts about data first approach to digital transformation. First of all, what does that mean? >> I think what we are seeing is that, if you... I think we do see that we are getting into a post digital era. Which means that in the last seven years, most bigger companies and businesses have invested in building a better customer engagement. What they did was they created properties, like portals, mobile applications, you name it, to just get better sense and touch their customers better than they were touching earlier. That was a whole investment that went in the last six, seven years. What they feel is that what's next. You do that, but does it really translate into revenue growth? Is it really translating into the experience in a sustained basis? Not one time, but on sustained basis. Every time when you touch a customer, they feel the same passion towards you. They feel that they are still engaged with you, and they want to come again to you for whatever your offering, your services or your goods. They felt that that's not actually happening. The reason why it's not happening is because the underlying data is not complete or comprehensive enough, or not accurate enough, for giving that experience. That realization is seeping up right now. They are asking for ensuring instead of looking at a use-case base approach of solving one problem for one business or one geography, is there a way to do it enterprise-wide? That a (mumbles). Point which is coming out is that they looked at that technology process that's old tradition model of looking at new businesses. Technology people processes and those three. But now they're looking to fourth element, which is foundation-call data. That's what we are calling data-first approach. You have to look at data as well, while looking at reforming your business services, and offers to the client. >> I want to touch on something you said earlier, and that is to make the customer feel passionate about interacting with you. I mean that's such a loaded, and almost romantic word to describe a customer interacting with a company. Why is it that companies are trying invoke passion, and insight passion, inspire passion? >> I think it's a way to differentiate yourself from the competition, so I think that's what in my view the businesses are doing right. Let me give an example to you to make it real, it may address your first question as well to some extent. We are working with a cruise company, one of the largest cruise companies North America based. They obviously are trying to make sure the experience of the customer is much better than had earlier. Which can resinate to a much higher revenue for them obviously, and inquisition of more customers. The friends of friends, friends of customers if you may. They had done a great job creating that digital property, and the transformation of the program. But they also realize that they are now, they realize that they don't really have a sense of who's the customer? Now that's a good question, after all this investment you still don't know who's the customer. That's where they came and talked about can I get a single view of my customer? The reason why they don't have a single view of customer is because they actually don't own all their individual customers. They only own their own individual customers, but they also work with their partners. As you can see Experian and others actually own that same customer. So they are not able to have a sense of that customer, their habits, and their behavior in one single place. They can really provide their accommodations, saying... well guest, if you're going to Italy we can probably help you this summer. >> So yes, exactly that's what I want to know; Is what, if you do have a sense of who your customer is, and that is everything from their basic demographic information, to what they do on Sunday afternoon with their families. What kinds of things then can the cruise company do to make that customer more passionate toward the cruise. >> They can do a lot, but I can tell you another example of another cruise company. Was looking at customer files and they did a fantastic job, and I'm assuming that you may have also experienced yourself. This customer they had covered the single view of customer obviously, but what they did was use a lot of IoT or sensors in their ships. They actually transformed the entire ship. Like the entire ship has been transformed to understand the customer movement, and give that flawless and seamless (mumbles) to customers. Which can help them have a pretty great on their vessels if you may. That's what they, from the day that you order the tickets for this service... From that time onward they actually send you a (mumbles). That tracks you as a person moving into the ship, and they can offer much more seamless services, and also reduces a friction of the operations staff. The staff is not in a hurry and hassle. They're actually able to understand who's actually the customer, what they want, and they are able to provide that service. So that's how they're using that feature of knowing the customer, to better serve them; being a better engagement with them. Plus also eases the operational friction in their own staff. >> So the customer wins because they feel the company gets them, and knows them, and understands them; and then the company wins because they're able to make more money off that customer, because they already have predicted what that customer wants and needs at every moment. >> And they can do more with less. They can do more with less staff, less resources. >> So one of that we are also talking a lot about here on theCUBE it's the tenth anniversary of theCUBE. So we've had a lot of these conversations, is how data is becoming a C-suite discussion, and there's this growing need to appoint a chief data officer to drive data strategy. What do you see as the evolving role of the CDO, at your company; and then also at the companies that you work with? >> We see this is a very significant step in the future. There are a lot of predictions from (mumbles) An analyst saying that there will be more and more roles, like three-fourth of the companies would have a CDO (mumbles). But I think our point is likely, you know, to augment that point I think what we believe is that, we do believe in respective of who actually owns (mumbles) That a chief data officer or a CI, or a CO. They definitely need a person at the C-suite, not below C-suite. To have that discussion at the table, and show that their data strategy is attached to their business strategy, and that's not true in many cases right now. So the data is (mumbles) which is two levels down in (mumbles), and that's why it's not getting that attention as a corporate asset as a (mumbles) asset from where you can actually extract value that you're looking at right. That's what we see; so we see a very broadened role, we see who so is in that role, we think there are a few qualities that person needs to have. The first one the person has to have a seat at the table. The second, is that person should be able to understand business quite well. (mumbles) He or she should have an insider business innovation, and if the person is tech savy it's good to have, but it's not must to have. We do believe that person should be able to prepare a strategy, and the governance of data across his or her peers. So they know that what value they are able to get from that data, and how they can share it across their functions. That's where the value comes in. Plus, beyond that the last point would be making sure whatever they do, they do responsibly. Do they actually make things work; whether it's using A.I., whether it's using any machine learning or anything else they have. They make sure that it's responsible data, and make it secure for themselves, for their enterprise, and for their customer. >> Well that is certainly a theme that we're hearing a lot about at Infomatica world. Tell me about the relationship between Accenture and Informatica. >> It's quite good, it's been good for years. We have been working together for years. The last two years, or two in a half years I think it has really taken a different shape within the new companies, and that's largely because we have really gone into a strategic discussion with the companies, and seeing what is the future. I think one thing that they are doing very well with their leadership. Anil himself is CEO; and Amit, and Tracy, and everybody else. And with our leadership is that we do believe that we are on the surface of un-tapping the value, one. Second thing is I don't think that used cases will draw the benefit which large organizations are looking at. It has to be something done at enterprise level. So think about like I think there another talk in the morning about enterprise data catalog. Amit was talking about, You need that. You need that to not do one used case for one particular business, for one particular country, or one particular customer segment. We need to do that for entire businesses across the enterprise. That can only happen if you have a sense of data, and you know how to do it effectively at scale. That's what I think that people are looking. Companies are going to be looking at the solution base, and I think it's the right timing for having the discussion. >> And there are going to be learnings that you can derive from financial services, and apply to retail, and healthcare, and all sorts of (mumble). Is that what you're finding here at Informatica World? Are you having those in conversations to learn the best practices? >> Oh yeah, I think we have our customers here; Accenture, as we have our customers here. we're presenting in different session. We had (mumbles) present today morning at eleven a.m. about how master data management can actually help you drive a better strategy on transforming your operations system like ESPE. That was never talked earlier, two years back nobody talked about saying how can MDM help you have a better transformation of your ESPE systems. Well that's where we are going. We are saying that, okay you have a trandiction systems, but you also need a system of right governance. Because all of your data, customer data or other data maybe sitting in ESPE or maybe sitting in sales force. How would you connect the dots? You need something to connect that dot so you have a single source of truth, and make sure that you know your customer, or vendor, or location, or everything else in the right fashion. >> Know your customer. So another thing I want to ask you about is the skills gap. I know that workforce of the future is something that you've worked passionately on. Passion keeps coming up in our conversation. (laughs amusingly) At Accenture. Tell us your story first in terms how you came to terms with this skill gap, and what you did at Accenture to remedy it. >> So this is four years back, and we were looking at our tech strategy, and our strategy to (mumbles) our business going forward, or where do we invest? And we are a people centric company so we are 470,000 people, that's a lot of people. In my role, one of the thing in my role is to make sure that I look at all the investment we do on our people. As CDO of our technology business, I need to make sure that we are investing in the right places. So this came to me saying that okay, will we be relevant as 470,000 people ten years from now? That's the question right? Because of A.I., because machine in our name, because people plus machine. What happens to our work force? So that's what I was trying to solve. Instead he's saying, what do we do next, and that was the whole point about workforce of the future. We will work more closely with the machines, and how will that happen. So what skills we will need as humans to work with machines, and everything else. What's going to happen in terms of automation going forward. And plus new talent which is required for the future. So we worked hard on this we built a strategy on what we need, then we did a very simple thing, we actually went to a high speed excursion, and agile sprints. We get it the few of principles actually. I can say a couple of them to use to resinate. One is the principle saying there's only (mumbles) available in the market. So don't spend creating stuff, but spend learning stuff. The second thing the chains of (mumbles) are a vision of our people vision, employee vision. It used to be saying, That you need to preform and grow. Something like that, if you preform high in our company, you'll grow faster. We changed the saying to learn and grow. So we said learning is more fundamental because performance will become automatic when you learn more. What we did was we changed. We worked really hard on the cultural aspect. And one of the things (mumbles) used to always say in the past ten years back, you used to learn a day in a month. Well that may not be enough today. Just because (mumbles) and the change of technology is much faster. It's 10x speed. So you can learn at 10x level, that doesn't mean you need to be learning at deep level for ten things, that's going to be hard for humans to do that. But you can use some help. That's what we do a 2 pronged approach. One is what we call a (mumble) training. Which means we make you more aware of everything that's happening in the world, and we give you a chance to support people-- >> I mean how do you do that, I mean that's a tall order. >> So what I did was we went to the market, and we looked at a lot of platforms. Okay you need technology to do everything. You get it right. You will be sitting here talking (mumbles). Using right technologies, right? Maybe show that our what we're talking is for (mumbles) people to watch us right. But the same thing there when we were looking at all the platforms. I looked at all things and I felt everything was great. (mumbles) It was not something which is exponential So I had to build a platform off it all, so I spent 6 months writing a whole platform. It was a really smart team, and all the logic I used was build a platform which treats or ploy a human in the center of your (mumbles) design. So we made a very personalized platform, where it helps a person to get there, and attracts you to come back. So it's very user friendly, or a very exponential platform. We call it Accenture Future Talent Platform. We deployed it across our entire businesses, we have 70+ number of people who are already being certified to their platform. They feel goof that they've gone to the next stage of their career. And now we are actually using the same platform for our clients. So we are giving them platforms so clients can use that effectively. >> From what I am hearing from you, it's about having technology skills, know how, and expertise. But also having this mindset of learning, and a hungry for learning, and wanting to know more. How do you make sure that, that culture is cultivated in the right way? >> We did some of the campaigns, so a very simple principle that we use is that like you do a marketing campaign to attract a customer. Whether he is selling a (mumbles), or selling a cruise experience, or vacation, or whatever. Use a similar principles for our own employers, and use it as learning campaigns. So marketing campaigns are learning campaigns. So one of the campaigns that we ran was, How important was it for you to be learning fit? So just like we always measure ourselves on health everyday, instead you measure yourself in learning. So our app was actually given to everybody, so you can see whether you are learning enough or not. We're in the culture of seeing how I'm doing against my own goals, but how am I doing against Rebecca's goal. >> Gameafying it, making it a little more fun. Making it a little competition. >> We also did (mumbles) as well, Because we felt that people look at their own models and say, well this person is very sexist, why would I want to be that person. That's a normal human. That's what people see so we made sure that our leaders do what they are saying. And they can buckle it down, they should start learning faster itself, from top management perspective. So people see them learning, they would say, I want to be like him. So that means I need to have the same behavior as this person. >> No, those are critical people in companies. Well, Sanjeev thank you so much for coming on theCUBE. It's been a pleasure having you. >> Same here, it was nice talking to you. >> I'm Rebecca Knight. You are watching theCUBE Informatica World 2019. (funky techno music)

(clicking noise) >> Hello, I'm John Furrier, SiliconANGLE media, co-host of theCUBE. We are here on the ground in, here in Santa Clara, California, Centrify's headquarters, with Tom Kemp, the CEO of Centrify, and Parham Eftekhari, who's the co-founder and senior fellow of ICIT, which is the Institute of Critical Infrastructure Technologies, here to talk about security conversation. Guys, welcome to theCUBE's On the Ground. >> Thank you. >> Great to be here. >> Great to see you again, Tom. >> Yeah, absolutely. >> And congratulations on all your success. And Parham, GovCloud is hot. We were just in D.C. with Amazon Web Services Public Sector Summit. It's gotten more and more to the point where cyber is in the front conversation, and the political conversation, but on the commercial side as well. There's incidents happening every day. Just this past month, HBO, Game of Thrones has been hijacked and ransomed. I guess that's ransom, or technically, and a hack. That's high-profile, but case after case of high-profile incidents. >> Yeah, yeah. >> Okay, on the commercial side. Public sector side, nobody knows what's happening. Why is security evolving slow right now? Why isn't it going faster? Can you guys talk about the state of the security market? >> Yeah, well, ya know, I think first of all, you have to look at the landscape. I mean, our public and private sector organizations are being pummeled every day by nation states, mercenaries, cyber criminals, script kiddies, cyber jihadists, and they're exploiting vulnerabilities that are inherent in our antiquated legacy systems that are put together by, ya know, with a Frankenstein network as well as devices and systems and apps that are built without security by design. And we're seeing the results, as you said, right? We're seeing an inundation of breaches on a daily basis, and many more that we don't hear about. We're seeing weaponized data that's being weaponized and used against us to make us question the integrity of our democratic process and we're seeing, now, a rise in the focus on what could be the outcome of a cyberkinetic incident, which, ultimately, in the worst case scenario, could have a loss of life. And so I think as we talk about cyber and what it is we're trying to accomplish as a community, we ultimately have a responsibility to elevate the conversation and make sure that it's not an option, but it is a priority. >> Yeah, no, look, I mean, here we are in a situation in which the industry is spending close to 80 billion dollars a year, and it's growing 10 percent, but the number of attacks are increasing much more than 10 percent, and as Parham said, you know, we literally had an election impacted by cyber security. It's on the front page with HBO, et cetera. And I really think that we're now in a situation where we really need to rethink how we do security in, as enterprises and as even individuals. >> And it's seems, talking about HBO, talking about the government, you mentioned, just the chaos that's going on here in America, you almost don't know what you don't know. And with the whole news cycle going on around this, but this gets back to this notion of critical infrastructure. I love that name, and you have in your title 'ICIT,' Institute of Critical Infrastructure, because, ya know, and certainly the government has had critical infrastructure. There's been bridges, and roads, and whatnot, they've had the DNS servers, there's been some critical infrastructure at the airports and whatnot, but for corporations, the critical infrastructure used to be the front door. And then their data center. Now with cloud, no perimeter, we've talked about this on theCUBE before, you start to change the notion of what critical infrastructure is. So, I guess, Parham, what does critical infrastructure mean, from a public and commercial perspective? Tell me, you can talk about it. And what's the priorities for the businesses and governments to figure out what's the order of operations to get to the bottom of making sure everything's secure? >> Yeah, it's interesting, that's a great question, you know, when most people think about critical infrastructure as legacy technology, or legacy's, you know, its roads, its bridges, its dams. But if you look at the Department of Homeland Security, they have 16 sectors that they're tasked with protecting. Includes healthcare, finance, energy, communications, right? So as we see technology start to become more and more ingrained in all these different sectors, and we're not just talking about data, we're talking about ICS data systems. A digital attack against any one of these critical infrastructure sectors is going to have different types of outcomes, whether you're talking about a commercial sector organization, or the government. You know, one of the things that we always talk about is really the importance of elevating the conversation, as I mentioned earlier, and putting security before profits. I think, ultimately, we've gotten to this situation because a lot of companies do a cost-benefit analysis, say, "You know what? I may be in the healthcare sector, "and ultimately it'll be cheaper for me to be breached, "pay my fines, and deal with potentially even the "loss to brand, to my brand, in terms of brand value, "and that'll cheaper than investing what "I need to to protect my patients and their information." And that's the wrong way to look at it. I think now, as we were talking about this week, the cost of all this is going higher, which is going to help, but I think we need to start seeing this fundamental mind-shift in how we are prioritizing security, as I mentioned earlier. It's not an option, it must be a requisite. >> Yeah, I think what we're seeing now, is in the years past, the hackers would get at some bits of information, but now we're seeing with HBO, with Sony, they can strip mine an entire company. >> They put them out of business. >> Exactly. >> The money that they're doing with ransomeware, which is a little bit higher profile, ransomware, I mean, there's a specific business outcome, here, and it's not looking good, they go out of business. >> Oh, absolutely, and so Centrify, we just recently sponsored a survey, and nowadays, if you announce that you got breached, and you have to, now. It's 'cause you have to tell your shareholders, you have to tell your customers. Your stock drops, on average, five percent in a day. And so we're talking about billions of dollars of market capitalization that can disappear with a breach as well. So we're beyond, it's like, "Oh, they stole some data, "we'll send out a letter to our customers, "and we'll give 'em free Experian for a year." Or something like that." Now, it's like, all your IP, all the content, and John, I think you raised a very good point, as well. In the case of the federal government, it's still about the infrastructure being physical items, and of course, with internet a thing since now it's connected to the internet, so it's really scary that a bridge can flip open by some guy in the Ukraine or Russia fiddling with it. But now with enterprises, it's less and less physical, the store, and we're now going through this massive shift to the cloud, and more and more of your IP is controlled and run. It's the complete deperimeterization that makes things every more complicated. >> Well it's interesting you mentioned the industrial aspect of it, with the bridge, because this is actually a real issue with self-driving cars, this was on everyone's mind, we were just covering some content, covering Ford's event yesterday in San Francisco. They got this huge problem. Ya know, hacking of the cars. So, industrial IOT opens up, again, the surface area, but this kind of brings the question down to customers, that you guys have or companies or governments. How do they become resilient? How do they put steps in place? Because, you know, I was just talking to someone who runs a major port in the U.S., and the issues there are maritime, right? So you talk about infrastructure, container ships, obviously worry about terrorists and other things happening. But just the general IT infrastructure is neanderthal, it's like, 30 years old. >> Yeah. >> So you have legacy infrastructure, as you mentioned, but businesses also have legacy, so how do you balance where you are? How do you know the progress bar of your protection? How do you know the things you need to put in place? How do you get to resilience? >> Yeah, but see, I think there also needs to be a rethink of security. Because the traditional ways that people did it, was protecting the perimeter, having antivirus, firewalls, et cetera. But things have really changed and so now what we're seeing is that an entity has become the top attack vector going in. And so if you look at all these hacks and breaches, it's the stealing of usernames and passwords, so people are doing a good job of, the hackers are social engineering the actual users, and so, kind of a focus needs to shift of securing the old perimeter, to focusing on securing the user. Is it really John Furrier trying to access e-mail? Can we leverage biometrics in this? And trying to move to the concept of a zero-trust model, and where you have to, can't trust the network, can't trust the IP address, but you need to factor in a lot of different aspects. >> It's interesting, I was just following this blog chain because we've been covering a lot of the blog chains, immutable and encrypted, the wallets were targets. (laughing) Hey, this Greta the Wall, where they store the money. Now we own that encrypted data. So, again, this is the, hackers are fast, so, again, back to companies because they have to put if they have shareholder issues, or they have some corporate governance issues. But at the end of the day, it's a moving train. How does the government offer support? How do companies put it in place? What do they need to do? >> Yeah, well, there's a couple of things you can look at. First of all, you know, as a think tank, we're active on Capital Hill, working with members of both minority and majority sides, we're actively proposing bipartisan legislation, which provides a meaningful movement forward to secure and address some of the issues you're talking about. Senator Markey recently put out the Cyber Shield Act, which creates a type of score, right? For a device, kind of like the ENERGY STAR in the energy sector. So just this week, ICIT put out a paper in support of an amendment by Senator Lindsey Graham, which actually addresses the inherent vulnerabilities in our election systems, right? So there's a lot of good work being done. And that really goes to the core of what we do, and the reasons that we're partnering together. ICIT is in the business of educating and advising. We put out research, we make it freely available, we don't believe in com`moditizing information, we believe in liberating it. So we get it in the hands of as many people as possible, and then we get this objective research, and use it as a stepping stone to educate and to advise. And it could be through meetings, it could be through events, it could be through conversation with the media. But I think this educational process is really critical to start to change the minds of-- >> You know, if I can add to that, I think what really needs to be done with security, is better information sharing. And it's with other governments and enterprises that are under attack. Sharing that information as opposed to only having it for themselves and their advantage, and then also what's required is better knowledge of what are the best practices that need to be done to better protect both government and enterprises. >> Well, guys, I want to shift gears and talk about the CyberConnect event, which is coming up in November, an industry event. You guys are sponsoring, Centrify, but you guys are also on the ball, there's a brand new content program. It's an independent event, it's targeted to the industry, not a Centrify user group. Parham, I want to put you on the spot before we get to the CyberConnect event. You mentioned the elections. What's the general, and I'm Silicon Valley and so I had to ask the question 'cause you're in the trenches down in D.C. What is the general sentiment in D.C. right now on the hacking? Because, I was explaining it to my son the other day, like, "Yeah, the Russians probably hacked everybody, "so technically the election "fell into that market basket of hats." So maybe they did hack you. So I'm just handwaving that, but it probably makes sense. The question is, how real is the hacking threat in the minds of the folks in D.C. around Russia and potentially China and these areas? >> Yeah, I think the threat is absolutely real, but I think there has to be a difference between media, on both sides, politicizing the conversation. There's a difference between somebody going in and actually, you know, changing your vote from one side to the other. There's also the conversation about the weaponization of data and what we do know that Russia is doing with regards to having armies of trolls out there or with fake profiles, and are creating faux conversations and steering public sentiment of perception in directions that maybe wasn't already there. And so I think part of the hysteria that we see, I think we're fearful and we have a right to be fearful, but I think taking the emotion and the politics out of it, and actually doing forensic assessments from an objective perspective to understanding what truly is going on. We are having our information stolen, there is a risk that a nation state could execute a very high-impact, digital attack that has a loss of life. We do know that foreign states are trying to impact the outcomes of our democratic processes. I think it's important to understand, though, how are they doing it and is what we're reading about truly what's happening kind of on the streets. >> And that's where the industrial thing you were kind of tying together, that's the loss of life potential, using digital as an attack vector into something that could have a physical, and ultimately deadly outcome. Yeah, we covered, also that story that was put out, about the fake news infrastructure. It's not just the content that they're making up, it's actually the infrastructure fake news. Bionets, and whatnot. And I think Mike Rowe wrote a story on this, where they actually detailed, you can smear a journalist for 40K. >> Yeah. >> These are actually out there, that are billed for specifically these counter... Programs. >> As a service. You know, go on a forum on the Deep Web and you can contract these types of things out. And it's absolutely out there. >> And then what do you say to your average American friends, that you're saying, hey, having a cocktail with, you're at a dinner. What's going on with security? What do you say to them? You should be worried, calm down, no we're on it. What's the message that you share with your friends that aren't in the industry? >> Personally, I think the message is that, you know, you need to vigilant, you need to, it may be annoying, but you do have to practice good cyber hygiene, think about your passwords, think about what you're sharing on social media. We'd also talk, and I personally believe that, some of these things will not change unless we as consumers change what is acceptable to us. If we stop buying devices or systems or apps based on the convenience that it brings to our lives, and we say, "I'm not going to spend money on that car, "because I don't know if it's secure enough for me." You will see industry change very quickly. So I think-- >> John: Consumer behavior is critical. >> Absolutely. That's definitely a piece of it. >> Alright, guys, so exciting event coming up, theCUBE will be covering the CyberConnect event in November. The dates, I think, November-- >> Sixth and seventh. >> Sixth and seventh in New York City at the Grand Hyatt. Talk about the curriculum, because this is a unique event, where you guys are bringing your sponsorship to the table, but providing an open industry event. What's the curriculum, what's the agenda, what's the purpose of the event? >> Yeah, Tom. >> Okay, I'll take it, yeah. I mean, historically, like other security vendors, we've had our users' conference, right? And what we've found is that, as you alluded to, that there just needs to be better education of what's going on. And so, instead of just limiting it to us talking to our customers about us, we really need to broaden the conversation. And so that's why we brought in ICIT, to really help us broaden the conversation, raise more awareness and visibility for what needs to be done. So this is a pretty unique conference in that we're having a lot of CSOs from some incredible enterprise, as well as government. General Alexander, the former of the Cyber Security Command is a keynote, but we have the CSO of Aetna, Blue Cross involved, as well. So we want to raise the awareness in terms of, what are the best practices? What are the leading minds thinking about security? And then parallel, also, for our customers, we're going to have a parallel track where, if they want to get more product-focused technology. So this is not a Centrify event. This is an industry event, ya know. Black Hat is great, RSA is great, but it's really more at the, kind of the bits and bytes-- >> They're very narrow, but you are only an identity player. There's a bigger issue. What about these other issues? Will you discuss-- >> Oh, absolutely. >> Yeah, well-- >> Is it an identity or is it more? >> It actually is more, and this is one of the reasons, at a macro level, the work that we've done at Centrify, for a number of years now. You know, we have shared the same philosophy that we have a responsibility, as experts in the cyberspace, to move the industry forward and to really usher in, almost a cyber security renaissance, if you will. And so, this is really the vision behind CyberConnect. So if you look at the curriculum, we're talking about, you know, corporate espionage, and how it's impacting commercial organizations. We're talking about the role of machine-learning based artificial intelligence. We'll be talking about the importance of encrypting your data. About security by design. About what's going on with the bot net epidemic that's out there. So there absolutely will be a very balanced program, and it is, again, driven and grounded in that research that ICIT is putting out in the relationships that we have with some of these key players. >> So you institute a critical infrastructure technology, the think tank that you're the co-founder of. You're bringing that broader agenda to CyberConnect. >> That's correct, absolutely. >> So this is awesome, congratulations, I got to ask, on the thought leadership side, you guys have been working together. Can you just talk about your relationship between Centrify and ICIT? So you're independent, you guys are a vendor. Talk about this relationship and why it's so important to this event. >> Well, absolutely. I mean, look, as a security vendor, you know, a lot of, a big percentage of security vendors sell into the U.S. federal government, and through those conversations that a lot of the CSOs at these governments were pointing at us to these ICIT guys, right? And we got awareness and visibility thought that. And it was like, they were just doing great stuff in terms of talking about, yes, Centrify is a leading identity provider, but people are looking for a complete solution, looking for a balanced way to look at it. And so we felt that it would be a great opportunity to partner with these guys. And so we sponsored an event that they did, Winter Summit. And then they did such a great job and the content was amazing, the people they had, that we said, "You know what? "Let's make this more of a general thing and "let's be in the background helping facilitate this, "but let the people hear about this good information." >> So you figured out the community model? (laughs) No, 'cause this is really what works. You got to enable, you're enabling this conversation, and more than ever in the security system, would love to get your perspective on this, is that there's an ethos developing, has been developed. And it's expanding aggressively. Kind of opens doors on one side, but security's all about data sharing. You mentioned that-- >> Yeah, absolutely. >> From a hacking standpoint, that's more of a statutory filing, but here, the security space is highly communicative. They talk to each other, and it's a trust relationship, so you're essentially bringing an independent event, you're funding it. >> Yeah, absolutely. >> It's not your event, this is an independent event. >> Absolutely. >> Yeah, and so Tom said it very well, as an institute, we rely on the financial capital that comes in from our partners, like Centrify. And so we would be unable to deliver at a large scale the value that we do to the legislative community, to federal agencies, and the commercial sector, and the institute's research is being shared on NATO libraries and embassies around the world. So this is really a global operation that we have. And so when we talk about layered security, right, we're not into a silver bullet solution. A lot of faux experts out there say, "I have the answer." We know that there's a layered approach that needs to be done. Centrify, they have the technology that plays a part in that, but, even more important than that for us is that they share that same philosophy and we do see ourselves as being able to usher in the changes required to move everything forward. And so it's been a great, you know, we have a lot of plans for the next few years. >> Yeah, that's great work, you're bringing in some great content to the table, and that's what people want, and they can see who's enabling it, that's a great business model for everyone. I got to ask one question, though, about your business. I love the critical infrastructure focus and I like your value you guys are bringing. But you guys have this fellow program. Can you just talk about this, 'cause your a part of the fellowship-- >> Yeah, absolutely. >> You're on a level, and I don't want to say credit 'cause you're not really going to get credit. But it's a badge, it's a bar. >> Yeah, yeah, no-- >> Explain the fellow program. >> That's a great question. At the institute, we have a core group of experts who represent different technology niches. They make up our fellow program, and so as I discussed earlier, when we're putting out research, when we're educating the media, when we're advising congress, when we're doing the work of the institute, we're constantly turning back to our fellow program members to provide some of that research and expertise. And sharing, you know, not just providing financial capital, but really bringing that thought leadership to the table. Centrify is a part of our fellows program, and so we've been working with them for a number of years. It's very exclusive and there's a process. You have to be referred in by an existing fellow program member. We have a lot of requests, but it really comes down to, do you understand what we're trying to accomplish? Do you share our same mission, our same values? And can you be part of this elite community that we've built? And so, you know, Centrify is a big part of that. >> And the cloud, obviously, is accelerating everything. You've got the cloud action, certainly, in your space, and we know what's going on in our world. >> Yeah, absolutely. >> The world is moving at a zillion miles an hour. It's like literally moving a train. So, congratulations, CyberConnect event in November. Great event, check it out, theCUBE will be there, we'll have live coverage, we broadcast, be documenting all the action and bringing it to you on theCUBE, obviously, (mumbles) John Furrier, here at Centrify's headquarters in California, in Silicon Valley, thanks for watching. (upbeat electronic music)