(Upbeat intro music) >> Thanks guys. Hi everybody. Welcome back to The Spheres. My name is Dave Vellante and you're watching theCubes continuous coverage of AWS storage day. I'm really excited to bring on Wayne Duso. Wayne is the vice-president of AWS Storage Edge and Data Governance Services. Wayne, two Boston boys got to come to Seattle to see each other. You know. Good to see you, man. >> Good to see you too. >> I mean, I'm not really from Boston. The guys from East Boston give me crap for saying that. [Wayne laughs] That my city, right? You're a city too. >> It's my city as well I'm from Charlestown so right across the ocean. >> Charlestown is actually legit Boston, you know I grew up in a town outside, but that's my city. So all the sports fan. So, hey great keynote today. We're going to unpack the keynote and, and really try to dig into it a little bit. You know, last 18 months has been a pretty bizarre, you know, who could have predicted this. We were just talking to my line about, you know, some of the permanent changes and, and even now it's like day to day, you're trying to figure out, okay, you know, what's next, you know, our business, your business. But, but clearly this has been an interesting time to say the least and the tailwind for the Cloud, but let's face it. How are customers responding? How are they changing their strategies as a result? >> Yeah. Well, first off, let me say it's good to see you. It's been years since we've been in chairs across from one another. >> Yeah. A couple of years ago in Boston, >> A couple of years ago in Boston. I'm glad to see you're doing well. >> Yeah. Thanks. You too. >> You look great. (Wayne Laughs) >> We get the Sox going. >> We'll be all set. >> Mm Dave you know, the last 18 months have been challenging. There's been a lot of change, but it's also been inspiring. What we've seen is our customers engaging the agility of the Cloud and appreciating the cost benefits of the Cloud. You know, during this time we've had to be there for our partners, our clients, our customers, and our people, whether it's work from home, whether it's expanding your capability, because it's surging say a company like zoom, where they're surging and they need more capability. Our cloud capabilities have allowed them to function, grow and thrive. In these challenging times. It's really a privilege that we have the services and we have the capability to enable people to behave and, execute and operate as normally as you possibly can in something that's never happened before in our lifetimes. It's unprecedented. It's a privilege. >> Yeah. I mean, I agree. You think about it. There's a lot of negative narrative, in the press about, about big tech and, and, and, you know, the reality is, is big tech has, has stood and small tech has stepped up big time and we were really think about it, Wayne, where would we be without, without tech? And I know it sounds bizarre, but we're kind of lucky. This pandemic actually occurred when it did, because had it occurred, you know, 10 years ago it would have been a lot tougher. I mean, who knows the state of vaccines, but certainly from a tech standpoint, the Cloud has been a savior. You've mentioned Zoom. I mean, you know, we, productivity continues. So that's been, been pretty key. I want to ask you, in you keynote, you talked about two paths to, to move to the Cloud, you know, Vector one was go and kind of lift and shift if I got it right. And then vector two was modernized first and then go, first of all, did I get that right? And >> Super close and >> So help me course correct. And what are those, what are those two paths mean for customers? How should we think about that? >> Yeah. So we want to make sure that customers can appreciate the value of the Cloud as quickly as they need to. And so there's, there's two paths and with not launches and, we'll talk about them in a minute, like our FSX for NetApp ONTAP, it allows customers to quickly move from like to like, so they can move from on-prem and what they're using in terms of the storage services, the processes they use to administer the data and manage the data straight onto AWS, without any conversion, without any change to their application. So I don't change to anything. So storage administrators can be really confident that they can move. Application Administrators know it will work as well, if not better with the Cloud. So moving onto AWS quickly to value that's one path. Now, once they move on to AWS, some customers will choose to modernize. So they will, they will modernize by containerizing their applications, or they will modernize by moving to server-less using Lambda, right? So that gives them the opportunity at the pace they want as quickly or as cautiously as they need to modernize their application, because they're already executing, they're already operating already getting value. Now within that context, then they can continue that modernization process by integrating with even more capabilities, whether it's ML capabilities or IOT capabilities, depending on their needs. So it's really about speed agility, the ability to innovate, and then the ability to get that flywheel going with cost optimization, feed those savings back into betterment for their customers. >> So how did the launches that you guys have made today and even, even previously, do they map into those two paths? >> Yeah, they do very well. >> How so? Help us understand that. >> So if we look, let's just run down through some of the launches today, >> Great. >> And we can, we can map those two, those two paths. So like we talked about FSX for NetApp ONTAP, or we just like to say FSX for ONTAP because it's so much easier to say. [Dave laughs] >> So FSX for ONTAP is a clear case of move. >> Right >> EBS io2 Block Express for Sand, a clear case of move. It allows customers to quickly move their sand workloads to AWS, with the launch of EBS direct API, supporting 64 terabyte volumes. Now you can snapshot your 64 terabyte volumes on-prem to already be in AWS, and you can restore them to an EBS io2 Block Express volume, allowing you to quickly move an ERP application or an Oracle application. Some enterprise application that requires the speed, the durability and the capability of VBS super quickly. So that's, those are good examples of, of that. In terms of the modernization path, our launch of AWS transfer managed workflows is a good example of that. Manage workflows have been around forever. >> Dave: Yeah. >> And, and customers rely on those workflows to run their business, but they really want to be able to take advantage of cloud capabilities. They want to be able to, for instance, apply ML to those workflows because it really kind of makes sense that their workloads are people related. You can apply artificial intelligence to them, >> Right >> This is an example of a service that allows them to modify those workflows, to modernize them and to build additional value into them. >> Well. I like that example. I got a couple of followup questions, if I may. Sticking on the machine learning and machine intelligence for a minute. That to me is a big one because when I was talking to my line about this is this, it's not just you sticking storage in a bucket anymore, right? You're invoking other services: machine intelligence, machine learning, might be database services, whatever it is, you know, streaming services. And it's a service, you know, there it is. It's not a real complicated integration. So that to me is big. I want to ask you about the block side of things >> Wayne: Sure >> You built in your day, a lot of boxes. >> Wayne: I've built a lot of boxes. >> And you know, the Sand space really well. >> Yeah. >> And you know, a lot of people probably more than I do storage admins that say you're not touching my Sand, right? And they just build a brick wall around it. Okay. And now eventually it ages out. And I think, you know, that whole cumbersome model it's understood, but nonetheless, their workloads and our apps are running on that. How do you see that movement from those and they're the toughest ones to move. The Oracle, the SAP they're really, you know, mission critical Microsoft apps, the database apps, hardcore stuff. How do you see that moving into the Cloud? Give us a sense as to what customers are telling you. >> Storage administrators have a hard job >> Dave: Yeah >> And trying to navigate how they move from on-prem to in Cloud is challenging. So we listened to the storage administrators, even when they tell us, No. we want to understand why no. And when you look at EBS io2 Block Express, this is in part our initial response to moving their saying into the Cloud super easily. Right? Because what do they need? They need performance. They need their ability. They need availability. They need the services to be able to snap and to be able to replicate their Capa- their storage. They need to know that they can move their applications without having to redo all they know to re-plan all they work on each and every day. They want to be able to move quickly and confidently. EBS io2 Block Express is the beginning of that. They can move confidently to sand in the Cloud using EBS. >> Well, so why do they say 'no'? Is it just like the inherent fear? Like a lawyer would say, don't do that, you know, don't or is it just, is it, is it a technical issue? Is it a cultural issue? And what are you seeing there? >> It's a cultural issue. It's a mindset issue, but it's a responsibility. I mean, these folks are responsible for the, one of the most important assets that you have. Most important asset for any company is people. Second most important asset is data. These folks are responsible for a very important asset. And if they don't get it right, if they don't get security, right. They don't get performance right. They don't get durability right. They don't get availability right. It's on them. So it's on us to make sure they're okay. >> Do you see it similar to the security discussion? Because early on, I was just talking to Sandy Carter about this and we were saying, you remember the CIA deal? Right? So I remember talking to the financial services people said, we'll never put any data in the Cloud. Okay they got to be one of your biggest industries, if not your biggest, you know customer base today. But there was fear and, and the CIA deal changed that. They're like, wow CIA is going to the Cloud They're really security conscious. And that was an example of maybe public sector informing commercial. Do you see it as similar? I mean there's obviously differences, but is it a sort of similar dynamic? >> I do. I do. You know, all of these ilities right. Whether it's, you know, durability, availability, security, we'll put ility at the end of that somehow. All of these are not jargon words. They mean something to each persona, to each customer. So we have to make sure that we address each of them. So like security. And we've been addressing the security concern since the beginning of AWS, because security is job number one. And operational excellence job number two. So, a lot of things we're talking about here is operational excellence, durability, availability, likeness are all operational concerns. And we have to make sure we deliver against those for our customers. >> I get it. I mean, the storage admins job is thankless, but the same time, you know, if your main expertise is managing LUNs, your growth path is limited. So they, they want to transform. They want to modernize their own careers. >> I love that. >> It's true. Right? I mean it's- >> Yeah. Yeah. So, you know, if you're a storage administrator today, understanding the storage portfolio that AWS delivers will allow you, and it will enable you empower you to be a cloud storage administrator. So you have no worry because you're, let's take FSX for ONTAP. You will take the skills that you've developed and honed over years and directly apply them to the workloads that you will bring to the Cloud. Using the same CLIs, The same APIs, the same consoles, the same capabilities. >> Plus you mentioned you guys announced, you talked about AWS backup services today, announced some stuff there. I see security governance, backup, identity access management, and governance. These are all adjacency. So if you're a, if you're a cloud storage administrator, you now are going to expand your scope of operations. You, you know, you're not going to be a security, Wiz overnight by any means, but you're now part of that, that rubric. And you're going to participate in that opportunity and learn some things and advance your career. I want to ask you, before we run out of time, you talked about agility and cost optimization, and it's kind of the yin and the yang of Cloud, if you will. But how are these seemingly conflicting forces in sync in your view. >> Like many things in life, right? [Wayne Laughs] >> We're going to get a little spiritually. >> We might get a little philosophical here. [Dave Laughs] >> You know, cloud announced, we've talked about two paths and in part of the two paths is enabling you to move quickly and be agile in how you move to the Cloud. Once you are on the Cloud, we have the ability through all of the service integrations that we have. In your ability to see exactly what's happening at every moment, to then cost optimize, to modernize, to cost optimize, to improve on the applications and workloads and data sets that you've brought. So this becomes a flywheel cost optimization allows you to reinvest, reinvest, be more agile, more innovative, which again, returns a value to your business and value to your customers. It's a flywheel effect. >> Yeah. It's kind of that gain sharing. Right? >> It is. >> And, you know, it's harder to do that in a, in an on-prem world, which everything is kind of, okay, it's working. Now boom, make it static. Oh, I want to bring in this capability or this, you know, AI. And then there's an integration challenge >> That's true. >> Going on. Not, not that there's, you know, there's differences in, APIs. But that's, to me is the opportunity to build on top of it. I just, again, talking to my line, I remember Andy Jassy saying, Hey, we purposefully have created our services at a really atomic level so that we can get down to the primitives and change as the market changes. To me, that's an opportunity for builders to create abstraction layers on top of that, you know, you've kind of, Amazon has kind of resisted that over the years, but, but almost on purpose. There's some of that now going on specialization and maybe certain industry solutions, but in general, your philosophy is to maintain that agility at the really granular level. >> It is, you know, we go back a long way. And as you said, I've built a lot of boxes and I'm proud of a lot of the boxes I've built, but a box is still a box, right? You have constraints. And when you innovate and build on the Cloud, when you move to the Cloud, you do not have those constraints, right? You have the agility, you can stand up a file system in three seconds, you can grow it and shrink it whenever you want. And you can delete it, get rid of it whenever you want back it up and then delete it. You don't have to worry about your infrastructure. You don't have to worry about is it going to be there in three months? It will be there in three seconds. So the agility of each of these services, the unique elements of all of these services allow you to capitalize on their value, use what you need and stop using it when you don't, and you don't have the same capabilities when you use more traditional products. >> So when you're designing a box, how is your mindset different than when you're designing a service? >> Well. You have physical constraints. You have to worry about the physical resources on that device for the life of that device, which is years. Think about what changes in three or five years. Think about the last two years alone and what's changed. Can you imagine having been constrained by only having boxes available to you during this last two years versus having the Cloud and being able to expand or contract based on your business needs, that would be really tough, right? And it has been tough. And that's why we've seen customers for every industry accelerate their use of the Cloud during these last two years. >> So I get that. So what's your mindset when you're building storage services and data services. >> So. Each of the surfaces that we have in object block file, movement services, data services, each of them provides very specific customer value and each are deeply integrated with the rest of AWS, so that when you need object services, you start using them. The integrations come along with you. When, if you're using traditional block, we talked about EBS io2 Block Express. When you're using file, just the example alone today with ONTAP, you know, you get to use what you need when you need it, and the way that you're used to using it without any concerns. >> (Dave mumbles) So your mindset is how do I exploit all these other services? You're like the chef and these are ingredients that you can tap and give a path to your customers to explore it over time. >> Yeah. Traditionally, for instance, if you were to have a filer, you would run multiple applications on that filer you're worried about. Cause you should, as a storage administrator, will each of those applications have the right amount of resources to run at peak. When you're on the Cloud, each of those applications will just spin up in seconds, their own file system. And those file systems can grow and shrink at whatever, however they need to do so. And you don't have to worry about one application interfering with the other application. It's not your concern anymore. And it's not really that fun to do. Anyway. It's kind of the hard work that nobody really you know, really wants to reward you for. So you can take your time and apply it to more business generate, you know, value for your business. >> That's great. Thank you for that. Okay. I'll I'll give you the last word. Give us the bumper sticker on AWS Storage day. Exciting day. The third AWS storage day. You guys keep getting bigger, raising the bar. >> And we're happy to keep doing it with you. >> Awesome. >> So thank you for flying out from Boston to see me. >> Pleasure, >> As they say. >> So, you know, this is a great opportunity for us to talk to customers, to thank them. It's a privilege to build what we build for customers. You know, our customers are leaders in their organizations and their businesses for their customers. And what we want to do is help them continue to be leaders and help them to continue to build and deliver we're here for them. >> Wayne. It's great to see you again. Thanks so much. >> Thanks. >> Maybe see you back at home. >> All right. Go Sox. All right. Yeah, go Sox. [Wayne Laughs] All right. Thank you for watching everybody. Back to Jenna Canal and Darko in the studio. Its Dave Volante. You're watching theCube. [Outro Music]

>>from around the globe. It's the Cube presenting adaptive data governance brought to you by Iota Ho. >>And we're back with the data automation. Siri's. In this episode, we're gonna learn more about what I owe Tahoe is doing in the field of adaptive data governance how it can help achieve business outcomes and mitigate data security risks. I'm Lisa Martin, and I'm joined by a J. Bihar on the CEO of Iot Tahoe and Lester Waters, the CEO of Bio Tahoe. Gentlemen, it's great to have you on the program. >>Thank you. Lisa is good to be back. >>Great. Staley's >>likewise very socially distant. Of course as we are. Listen, we're gonna start with you. What's going on? And I am Tahoe. What's name? Well, >>I've been with Iot Tahoe for a little over the year, and one thing I've learned is every customer needs air just a bit different. So we've been working on our next major release of the I O. Tahoe product. But to really try to address these customer concerns because, you know, we wanna we wanna be flexible enough in order to come in and not just profile the date and not just understand data quality and lineage, but also to address the unique needs of each and every customer that we have. And so that required a platform rewrite of our product so that we could, uh, extend the product without building a new version of the product. We wanted to be able to have plausible modules. We also focused a lot on performance. That's very important with the bulk of data that we deal with that we're able to pass through that data in a single pass and do the analytics that are needed, whether it's, uh, lineage, data quality or just identifying the underlying data. And we're incorporating all that we've learned. We're tuning up our machine learning we're analyzing on MAWR dimensions than we've ever done before. We're able to do data quality without doing a Nen initial rejects for, for example, just out of the box. So I think it's all of these things were coming together to form our next version of our product. We're really excited by it, >>So it's exciting a J from the CEO's level. What's going on? >>Wow, I think just building on that. But let's still just mentioned there. It's were growing pretty quickly with our partners. And today, here with Oracle are excited. Thio explain how that shaping up lots of collaboration already with Oracle in government, in insurance, on in banking and we're excited because we get to have an impact. It's real satisfying to see how we're able. Thio. Help businesses transform, Redefine what's possible with their data on bond. Having I recall there is a partner, uh, to lean in with is definitely helping. >>Excellent. We're gonna dig into that a little bit later. Let's let's go back over to you. Explain adaptive data governance. Help us understand that >>really adaptive data governance is about achieving business outcomes through automation. It's really also about establishing a data driven culture and pushing what's traditionally managed in I t out to the business. And to do that, you've got to you've got Thio. You've got to enable an environment where people can actually access and look at the information about the data, not necessarily access the underlying data because we've got privacy concerns itself. But they need to understand what kind of data they have, what shape it's in what's dependent on it upstream and downstream, and so that they could make their educated decisions on on what they need to do to achieve those business outcomes. >>Ah, >>lot of a lot of frameworks these days are hardwired, so you can set up a set of business rules, and that set of business rules works for a very specific database and a specific schema. But imagine a world where you could just >>say, you >>know, the start date of alone must always be before the end date of alone and having that generic rule, regardless of the underlying database and applying it even when a new database comes online and having those rules applied. That's what adaptive data governance about I like to think of. It is the intersection of three circles, Really. It's the technical metadata coming together with policies and rules and coming together with the business ontology ease that are that are unique to that particular business. And this all of this. Bringing this all together allows you to enable rapid change in your environment. So it's a mouthful, adaptive data governance. But that's what it kind of comes down to. >>So, Angie, help me understand this. Is this book enterprise companies are doing now? Are they not quite there yet. >>Well, you know, Lisa, I think every organization is is going at its pace. But, you know, markets are changing the economy and the speed at which, um, some of the changes in the economy happening is is compelling more businesses to look at being more digital in how they serve their own customers. Eh? So what we're seeing is a number of trends here from heads of data Chief Data Officers, CEO, stepping back from, ah, one size fits all approach because they've tried that before, and it it just hasn't worked. They've spent millions of dollars on I T programs China Dr Value from that data on Bennett. And they've ended up with large teams of manual processing around data to try and hardwire these policies to fit with the context and each line of business and on that hasn't worked. So the trends that we're seeing emerge really relate. Thio, How do I There's a chief data officer as a CEO. Inject more automation into a lot of these common tax. Andi, you know, we've been able toc that impact. I think the news here is you know, if you're trying to create a knowledge graph a data catalog or Ah, business glossary. And you're trying to do that manually will stop you. You don't have to do that manually anymore. I think best example I can give is Lester and I We we like Chinese food and Japanese food on. If you were sitting there with your chopsticks, you wouldn't eat the bowl of rice with the chopsticks, one grain at a time. What you'd want to do is to find a more productive way to to enjoy that meal before it gets cold. Andi, that's similar to how we're able to help the organizations to digest their data is to get through it faster, enjoy the benefits of putting that data to work. >>And if it was me eating that food with you guys, I would be not using chopsticks. I would be using a fork and probably a spoon. So eso Lester, how then does iota who go about doing this and enabling customers to achieve this? >>Let me, uh, let me show you a little story have here. So if you take a look at the challenges the most customers have, they're very similar, but every customers on a different data journey, so but it all starts with what data do I have? What questions or what shape is that data in? Uh, how is it structured? What's dependent on it? Upstream and downstream. Um, what insights can I derive from that data? And how can I answer all of those questions automatically? So if you look at the challenges for these data professionals, you know, they're either on a journey to the cloud. Maybe they're doing a migration oracle. Maybe they're doing some data governance changes on bits about enabling this. So if you look at these challenges and I'm gonna take you through a >>story here, E, >>I want to introduce Amanda. Man does not live like, uh, anyone in any large organization. She's looking around and she just sees stacks of data. I mean, different databases, the one she knows about, the one she doesn't know about what should know about various different kinds of databases. And a man is just tasking with understanding all of this so that they can embark on her data journey program. So So a man who goes through and she's great. I've got some handy tools. I can start looking at these databases and getting an idea of what we've got. Well, as she digs into the databases, she starts to see that not everything is as clear as she might have hoped it would be. You know, property names or column names, or have ambiguous names like Attribute one and attribute to or maybe date one and date to s Oh, man is starting to struggle, even though she's get tools to visualize. And look what look at these databases. She still No, she's got a long road ahead. And with 2000 databases in her large enterprise, yes, it's gonna be a long turkey but Amanda Smart. So she pulls out her trusty spreadsheet to track all of her findings on what she doesn't know about. She raises a ticket or maybe tries to track down the owner to find what the data means. And she's tracking all this information. Clearly, this doesn't scale that well for Amanda, you know? So maybe organization will get 10 Amanda's to sort of divide and conquer that work. But even that doesn't work that well because they're still ambiguities in the data with Iota ho. What we do is we actually profile the underlying data. By looking at the underlying data, we can quickly see that attribute. One looks very much like a U. S. Social Security number and attribute to looks like a I c D 10 medical code. And we do this by using anthologies and dictionaries and algorithms to help identify the underlying data and then tag it. Key Thio Doing, uh, this automation is really being able to normalize things across different databases, so that where there's differences in column names, I know that in fact, they contain contain the same data. And by going through this exercise with a Tahoe, not only can we identify the data, but we also could gain insights about the data. So, for example, we can see that 97% of that time that column named Attribute one that's got us Social Security numbers has something that looks like a Social Security number. But 3% of the time, it doesn't quite look right. Maybe there's a dash missing. Maybe there's a digit dropped. Or maybe there's even characters embedded in it. So there may be that may be indicative of a data quality issues, so we try to find those kind of things going a step further. We also try to identify data quality relationships. So, for example, we have two columns, one date, one date to through Ah, observation. We can see that date 1 99% of the time is less than date, too. 1% of the time. It's not probably indicative of a data quality issue, but going a step further, we can also build a business rule that says Day one is less than date to. And so then when it pops up again, we can quickly identify and re mediate that problem. So these are the kinds of things that we could do with with iota going even a step further. You could take your your favorite data science solution production ISAT and incorporated into our next version a zey what we call a worker process to do your own bespoke analytics. >>We spoke analytics. Excellent, Lester. Thank you. So a J talk us through some examples of where you're putting this to use. And also what is some of the feedback from >>some customers? But I think it helped do this Bring it to life a little bit. Lisa is just to talk through a case study way. Pull something together. I know it's available for download, but in ah, well known telecommunications media company, they had a lot of the issues that lasted. You spoke about lots of teams of Amanda's, um, super bright data practitioners, um, on baby looking to to get more productivity out of their day on, deliver a good result for their own customers for cell phone subscribers, Um, on broadband users. So you know that some of the examples that we can see here is how we went about auto generating a lot of that understanding off that data within hours. So Amanda had her data catalog populated automatically. A business class three built up on it. Really? Then start to see. Okay, where do I want Thio? Apply some policies to the data to to set in place some controls where they want to adapt, how different lines of business, maybe tax versus customer operations have different access or permissions to that data on What we've been able to do there is, is to build up that picture to see how does data move across the entire organization across the state. Andi on monitor that overtime for improvement, so have taken it from being a reactive. Let's do something Thio. Fix something. Thio, Now more proactive. We can see what's happening with our data. Who's using it? Who's accessing it, how it's being used, how it's being combined. Um, on from there. Taking a proactive approach is a real smart use of of the talents in in that telco organization Onda folks that worked there with data. >>Okay, Jason, dig into that a little bit deeper. And one of the things I was thinking when you were talking through some of those outcomes that you're helping customers achieve is our ally. How do customers measure are? Why? What are they seeing with iota host >>solution? Yeah, right now that the big ticket item is time to value on. And I think in data, a lot of the upfront investment cause quite expensive. They have been today with a lot of the larger vendors and technologies. So what a CEO and economic bio really needs to be certain of is how quickly can I get that are away. I think we've got something we can show. Just pull up a before and after, and it really comes down to hours, days and weeks. Um, where we've been able Thio have that impact on in this playbook that we pulled together before and after picture really shows. You know, those savings that committed a bit through providing data into some actionable form within hours and days to to drive agility, but at the same time being out and forced the controls to protect the use of that data who has access to it. So these are the number one thing I'd have to say. It's time on. We can see that on the the graphic that we've just pulled up here. >>We talk about achieving adaptive data governance. Lester, you guys talk about automation. You talk about machine learning. How are you seeing those technologies being a facilitator of organizations adopting adaptive data governance? Well, >>Azaz, we see Mitt Emmanuel day. The days of manual effort are so I think you know this >>is a >>multi step process. But the very first step is understanding what you have in normalizing that across your data estate. So you couple this with the ontology, that air unique to your business. There is no algorithms, and you basically go across and you identify and tag tag that data that allows for the next steps toe happen. So now I can write business rules not in terms of columns named columns, but I could write him in terms of the tags being able to automate. That is a huge time saver and the fact that we can suggest that as a rule, rather than waiting for a person to come along and say, Oh, wow. Okay, I need this rule. I need this will thes air steps that increased that are, I should say, decrease that time to value that A. J talked about and then, lastly, a couple of machine learning because even with even with great automation and being able to profile all of your data and getting a good understanding, that brings you to a certain point. But there's still ambiguities in the data. So, for example, I might have to columns date one and date to. I may have even observed the date. One should be less than day two, but I don't really know what date one and date to our other than a date. So this is where it comes in, and I might ask the user said, >>Can >>you help me identify what date? One and date You are in this in this table. Turns out they're a start date and an end date for alone That gets remembered, cycled into the machine learning. So if I start to see this pattern of date one day to elsewhere, I'm going to say, Is it start dating and date? And these Bringing all these things together with this all this automation is really what's key to enabling this This'll data governance. Yeah, >>great. Thanks. Lester and a j wanna wrap things up with something that you mentioned in the beginning about what you guys were doing with Oracle. Take us out by telling us what you're doing there. How are you guys working together? >>Yeah, I think those of us who worked in i t for many years we've We've learned Thio trust articles technology that they're shifting now to ah, hybrid on Prohm Cloud Generation to platform, which is exciting. Andi on their existing customers and new customers moving to article on a journey. So? So Oracle came to us and said, you know, we can see how quickly you're able to help us change mindsets Ondas mindsets are locked in a way of thinking around operating models of I t. That there may be no agile and what siloed on day wanting to break free of that and adopt a more agile A p I at driven approach. A lot of the work that we're doing with our recall no is around, uh, accelerating what customers conduce with understanding their data and to build digital APS by identifying the the underlying data that has value. Onda at the time were able to do that in in in hours, days and weeks. Rather many months. Is opening up the eyes to Chief Data Officers CEO to say, Well, maybe we can do this whole digital transformation this year. Maybe we can bring that forward and and transform who we are as a company on that's driving innovation, which we're excited about it. I know Oracle, a keen Thio to drive through and >>helping businesses transformed digitally is so incredibly important in this time as we look Thio things changing in 2021 a. J. Lester thank you so much for joining me on this segment explaining adaptive data governance, how organizations can use it benefit from it and achieve our Oi. Thanks so much, guys. >>Thank you. Thanks again, Lisa. >>In a moment, we'll look a adaptive data governance in banking. This is the Cube, your global leader in high tech coverage. >>Innovation, impact influence. Welcome to the Cube. Disruptors. Developers and practitioners learn from the voices of leaders who share their personal insights from the hottest digital events around the globe. Enjoy the best this community has to offer on the Cube, your global leader in high tech digital coverage. >>Our next segment here is an interesting panel you're gonna hear from three gentlemen about adaptive data. Governments want to talk a lot about that. Please welcome Yusuf Khan, the global director of data services for Iot Tahoe. We also have Santiago Castor, the chief data officer at the First Bank of Nigeria, and good John Vander Wal, Oracle's senior manager of digital transformation and industries. Gentlemen, it's great to have you joining us in this in this panel. Great >>to be >>tried for me. >>Alright, Santiago, we're going to start with you. Can you talk to the audience a little bit about the first Bank of Nigeria and its scale? This is beyond Nigeria. Talk to us about that. >>Yes, eso First Bank of Nigeria was created 125 years ago. One of the oldest ignored the old in Africa because of the history he grew everywhere in the region on beyond the region. I am calling based in London, where it's kind of the headquarters and it really promotes trade, finance, institutional banking, corporate banking, private banking around the world in particular, in relationship to Africa. We are also in Asia in in the Middle East. >>So, Sanjay, go talk to me about what adaptive data governance means to you. And how does it help the first Bank of Nigeria to be able to innovate faster with the data that you have? >>Yes, I like that concept off adaptive data governor, because it's kind of Ah, I would say an approach that can really happen today with the new technologies before it was much more difficult to implement. So just to give you a little bit of context, I I used to work in consulting for 16, 17 years before joining the president of Nigeria, and I saw many organizations trying to apply different type of approaches in the governance on by the beginning early days was really kind of a year. A Chicago A. A top down approach where data governance was seeing as implement a set of rules, policies and procedures. But really, from the top down on is important. It's important to have the battle off your sea level of your of your director. Whatever I saw, just the way it fails, you really need to have a complimentary approach. You can say bottom are actually as a CEO are really trying to decentralize the governor's. Really, Instead of imposing a framework that some people in the business don't understand or don't care about it, it really needs to come from them. So what I'm trying to say is that data basically support business objectives on what you need to do is every business area needs information on the detector decisions toe actually be able to be more efficient or create value etcetera. Now, depending on the business questions they have to solve, they will need certain data set. So they need actually to be ableto have data quality for their own. For us now, when they understand that they become the stores naturally on their own data sets. And that is where my bottom line is meeting my top down. You can guide them from the top, but they need themselves to be also empower and be actually, in a way flexible to adapt the different questions that they have in orderto be able to respond to the business needs. Now I cannot impose at the finish for everyone. I need them to adapt and to bring their answers toe their own business questions. That is adaptive data governor and all That is possible because we have. And I was saying at the very beginning just to finalize the point, we have new technologies that allow you to do this method data classifications, uh, in a very sophisticated way that you can actually create analitico of your metadata. You can understand your different data sources in order to be able to create those classifications like nationalities, a way of classifying your customers, your products, etcetera. >>So one of the things that you just said Santa kind of struck me to enable the users to be adaptive. They probably don't want to be logging in support ticket. So how do you support that sort of self service to meet the demand of the users so that they can be adaptive. >>More and more business users wants autonomy, and they want to basically be ableto grab the data and answer their own question. Now when you have, that is great, because then you have demand of businesses asking for data. They're asking for the insight. Eso How do you actually support that? I would say there is a changing culture that is happening more and more. I would say even the current pandemic has helped a lot into that because you have had, in a way, off course, technology is one of the biggest winners without technology. We couldn't have been working remotely without these technologies where people can actually looking from their homes and still have a market data marketplaces where they self serve their their information. But even beyond that data is a big winner. Data because the pandemic has shown us that crisis happened, that we cannot predict everything and that we are actually facing a new kind of situation out of our comfort zone, where we need to explore that we need to adapt and we need to be flexible. How do we do that with data. Every single company either saw the revenue going down or the revenue going very up For those companies that are very digital already. Now it changed the reality, so they needed to adapt. But for that they needed information. In order to think on innovate, try toe, create responses So that type of, uh, self service off data Haider for data in order to be able to understand what's happening when the prospect is changing is something that is becoming more, uh, the topic today because off the condemning because of the new abilities, the technologies that allow that and then you then are allowed to basically help your data. Citizens that call them in the organization people that no other business and can actually start playing and an answer their own questions. Eso so these technologies that gives more accessibility to the data that is some cataloging so they can understand where to go or what to find lineage and relationships. All this is is basically the new type of platforms and tools that allow you to create what are called a data marketplace. I think these new tools are really strong because they are now allowing for people that are not technology or I t people to be able to play with data because it comes in the digital world There. Used to a given example without your who You have a very interesting search functionality. Where if you want to find your data you want to sell, Sir, you go there in that search and you actually go on book for your data. Everybody knows how to search in Google, everybody's searching Internet. So this is part of the data culture, the digital culture. They know how to use those schools. Now, similarly, that data marketplace is, uh, in you can, for example, see which data sources they're mostly used >>and enabling that speed that we're all demanding today during these unprecedented times. Goodwin, I wanted to go to you as we talk about in the spirit of evolution, technology is changing. Talk to us a little bit about Oracle Digital. What are you guys doing there? >>Yeah, Thank you. Um, well, Oracle Digital is a business unit that Oracle EMEA on. We focus on emerging countries as well as low and enterprises in the mid market, in more developed countries and four years ago. This started with the idea to engage digital with our customers. Fear Central helps across EMEA. That means engaging with video, having conference calls, having a wall, a green wall where we stand in front and engage with our customers. No one at that time could have foreseen how this is the situation today, and this helps us to engage with our customers in the way we were already doing and then about my team. The focus of my team is to have early stage conversations with our with our customers on digital transformation and innovation. And we also have a team off industry experts who engaged with our customers and share expertise across EMEA, and we inspire our customers. The outcome of these conversations for Oracle is a deep understanding of our customer needs, which is very important so we can help the customer and for the customer means that we will help them with our technology and our resource is to achieve their goals. >>It's all about outcomes, right? Good Ron. So in terms of automation, what are some of the things Oracle's doing there to help your clients leverage automation to improve agility? So that they can innovate faster, which in these interesting times it's demanded. >>Yeah, thank you. Well, traditionally, Oracle is known for their databases, which have bean innovated year over year. So here's the first lunch on the latest innovation is the autonomous database and autonomous data warehouse. For our customers, this means a reduction in operational costs by 90% with a multi medal converts, database and machine learning based automation for full life cycle management. Our databases self driving. This means we automate database provisioning, tuning and scaling. The database is self securing. This means ultimate data protection and security, and it's self repairing the automates failure, detection fail over and repair. And then the question is for our customers, What does it mean? It means they can focus on their on their business instead off maintaining their infrastructure and their operations. >>That's absolutely critical use if I want to go over to you now. Some of the things that we've talked about, just the massive progression and technology, the evolution of that. But we know that whether we're talking about beta management or digital transformation, a one size fits all approach doesn't work to address the challenges that the business has, um that the i t folks have, as you're looking through the industry with what Santiago told us about first Bank of Nigeria. What are some of the changes that you're seeing that I owe Tahoe seeing throughout the industry? >>Uh, well, Lisa, I think the first way I'd characterize it is to say, the traditional kind of top down approach to data where you have almost a data Policeman who tells you what you can and can't do, just doesn't work anymore. It's too slow. It's too resource intensive. Uh, data management data, governments, digital transformation itself. It has to be collaborative on. There has to be in a personalization to data users. Um, in the environment we find ourselves in. Now, it has to be about enabling self service as well. Um, a one size fits all model when it comes to those things around. Data doesn't work. As Santiago was saying, it needs to be adapted toe how the data is used. Andi, who is using it on in order to do this cos enterprises organizations really need to know their data. They need to understand what data they hold, where it is on what the sensitivity of it is they can then any more agile way apply appropriate controls on access so that people themselves are and groups within businesses are our job and could innovate. Otherwise, everything grinds to a halt, and you risk falling behind your competitors. >>Yeah, that one size fits all term just doesn't apply when you're talking about adaptive and agility. So we heard from Santiago about some of the impact that they're making with First Bank of Nigeria. Used to talk to us about some of the business outcomes that you're seeing other customers make leveraging automation that they could not do >>before it's it's automatically being able to classify terabytes, terabytes of data or even petabytes of data across different sources to find duplicates, which you can then re mediate on. Deletes now, with the capabilities that iota offers on the Oracle offers, you can do things not just where the five times or 10 times improvement, but it actually enables you to do projects for Stop that otherwise would fail or you would just not be able to dio I mean, uh, classifying multi terrible and multi petabytes states across different sources, formats very large volumes of data in many scenarios. You just can't do that manually. I mean, we've worked with government departments on the issues there is expect are the result of fragmented data. There's a lot of different sources. There's lot of different formats and without these newer technologies to address it with automation on machine learning, the project isn't durable. But now it is on that that could lead to a revolution in some of these businesses organizations >>to enable that revolution that there's got to be the right cultural mindset. And one of the when Santiago was talking about folks really kind of adapted that. The thing I always call that getting comfortably uncomfortable. But that's hard for organizations to. The technology is here to enable that. But well, you're talking with customers use. How do you help them build the trust in the confidence that the new technologies and a new approaches can deliver what they need? How do you help drive the kind of a tech in the culture? >>It's really good question is because it can be quite scary. I think the first thing we'd start with is to say, Look, the technology is here with businesses like I Tahoe. Unlike Oracle, it's already arrived. What you need to be comfortable doing is experimenting being agile around it, Andi trying new ways of doing things. Uh, if you don't wanna get less behind that Santiago on the team that fbn are a great example off embracing it, testing it on a small scale on, then scaling up a Toyota, we offer what we call a data health check, which can actually be done very quickly in a matter of a few weeks. So we'll work with a customer. Picky use case, install the application, uh, analyzed data. Drive out Cem Cem quick winds. So we worked in the last few weeks of a large entity energy supplier, and in about 20 days, we were able to give them an accurate understanding of their critical data. Elements apply. Helping apply data protection policies. Minimize copies of the data on work out what data they needed to delete to reduce their infrastructure. Spend eso. It's about experimenting on that small scale, being agile on, then scaling up in a kind of very modern way. >>Great advice. Uh, Santiago, I'd like to go back to Is we kind of look at again that that topic of culture and the need to get that mindset there to facilitate these rapid changes, I want to understand kind of last question for you about how you're doing that from a digital transformation perspective. We know everything is accelerating in 2020. So how are you building resilience into your data architecture and also driving that cultural change that can help everyone in this shift to remote working and a lot of the the digital challenges and changes that we're all going through? >>The new technologies allowed us to discover the dating anyway. Toe flawed and see very quickly Information toe. Have new models off over in the data on giving autonomy to our different data units. Now, from that autonomy, they can then compose an innovator own ways. So for me now, we're talking about resilience because in a way, autonomy and flexibility in a organization in a data structure with platform gives you resilience. The organizations and the business units that I have experienced in the pandemic are working well. Are those that actually because they're not physically present during more in the office, you need to give them their autonomy and let them actually engaged on their own side that do their own job and trust them in a way on as you give them, that they start innovating and they start having a really interesting ideas. So autonomy and flexibility. I think this is a key component off the new infrastructure. But even the new reality that on then it show us that, yes, we used to be very kind off structure, policies, procedures as very important. But now we learn flexibility and adaptability of the same side. Now, when you have that a key, other components of resiliency speed, because people want, you know, to access the data and access it fast and on the site fast, especially changes are changing so quickly nowadays that you need to be ableto do you know, interact. Reiterate with your information to answer your questions. Pretty, um, so technology that allows you toe be flexible iterating on in a very fast job way continue will allow you toe actually be resilient in that way, because you are flexible, you adapt your job and you continue answering questions as they come without having everything, setting a structure that is too hard. We also are a partner off Oracle and Oracle. Embodies is great. They have embedded within the transactional system many algorithms that are allowing us to calculate as the transactions happened. What happened there is that when our customers engaged with algorithms and again without your powers, well, the machine learning that is there for for speeding the automation of how you find your data allows you to create a new alliance with the machine. The machine is their toe, actually, in a way to your best friend to actually have more volume of data calculated faster. In a way, it's cover more variety. I mean, we couldn't hope without being connected to this algorithm on >>that engagement is absolutely critical. Santiago. Thank you for sharing that. I do wanna rap really quickly. Good On one last question for you, Santiago talked about Oracle. You've talked about a little bit. As we look at digital resilience, talk to us a little bit in the last minute about the evolution of Oracle. What you guys were doing there to help your customers get the resilience that they have toe have to be not just survive but thrive. >>Yeah. Oracle has a cloud offering for infrastructure, database, platform service and a complete solutions offered a South on Daz. As Santiago also mentioned, We are using AI across our entire portfolio and by this will help our customers to focus on their business innovation and capitalize on data by enabling new business models. Um, and Oracle has a global conference with our cloud regions. It's massively investing and innovating and expanding their clouds. And by offering clouds as public cloud in our data centers and also as private cloud with clouded customer, we can meet every sovereignty and security requirements. And in this way we help people to see data in new ways. We discover insights and unlock endless possibilities. And and maybe 11 of my takeaways is if I If I speak with customers, I always tell them you better start collecting your data. Now we enable this partners like Iota help us as well. If you collect your data now, you are ready for tomorrow. You can never collect your data backwards, So that is my take away for today. >>You can't collect your data backwards. Excellently, John. Gentlemen, thank you for sharing all of your insights. Very informative conversation in a moment, we'll address the question. Do you know your data? >>Are you interested in test driving the iota Ho platform kick Start the benefits of data automation for your business through the Iota Ho Data Health check program. Ah, flexible, scalable sandbox environment on the cloud of your choice with set up service and support provided by Iota ho. Look time with a data engineer to learn more and see Io Tahoe in action from around the globe. It's the Cube presenting adaptive data governance brought to you by Iota Ho. >>In this next segment, we're gonna be talking to you about getting to know your data. And specifically you're gonna hear from two folks at Io Tahoe. We've got enterprise account execs to be to Davis here, as well as Enterprise Data engineer Patrick Simon. They're gonna be sharing insights and tips and tricks for how you could get to know your data and quickly on. We also want to encourage you to engage with the media and Patrick, use the chat feature to the right, send comments, questions or feedback so you can participate. All right, Patrick Savita, take it away. Alright. >>Thankfully saw great to be here as Lisa mentioned guys, I'm the enterprise account executive here in Ohio. Tahoe you Pat? >>Yeah. Hey, everyone so great to be here. I said my name is Patrick Samit. I'm the enterprise data engineer here in Ohio Tahoe. And we're so excited to be here and talk about this topic as one thing we're really trying to perpetuate is that data is everyone's business. >>So, guys, what patent I got? I've actually had multiple discussions with clients from different organizations with different roles. So we spoke with both your technical and your non technical audience. So while they were interested in different aspects of our platform, we found that what they had in common was they wanted to make data easy to understand and usable. So that comes back. The pats point off to being everybody's business because no matter your role, we're all dependent on data. So what Pan I wanted to do today was wanted to walk you guys through some of those client questions, slash pain points that we're hearing from different industries and different rules and demo how our platform here, like Tahoe, is used for automating Dozier related tasks. So with that said are you ready for the first one, Pat? >>Yeah, Let's do it. >>Great. So I'm gonna put my technical hat on for this one. So I'm a data practitioner. I just started my job. ABC Bank. I have, like, over 100 different data sources. So I have data kept in Data Lakes, legacy data, sources, even the cloud. So my issue is I don't know what those data sources hold. I don't know what data sensitive, and I don't even understand how that data is connected. So how can I saw who help? >>Yeah, I think that's a very common experience many are facing and definitely something I've encountered in my past. Typically, the first step is to catalog the data and then start mapping the relationships between your various data stores. Now, more often than not, this has tackled through numerous meetings and a combination of excel and something similar to video which are too great tools in their own part. But they're very difficult to maintain. Just due to the rate that we are creating data in the modern world. It starts to beg for an idea that can scale with your business needs. And this is where a platform like Io Tahoe becomes so appealing, you can see here visualization of the data relationships created by the I. O. Tahoe service. Now, what is fantastic about this is it's not only laid out in a very human and digestible format in the same action of creating this view, the data catalog was constructed. >>Um so is the data catalog automatically populated? Correct. Okay, so So what I'm using Iota hope at what I'm getting is this complete, unified automated platform without the added cost? Of course. >>Exactly. And that's at the heart of Iota Ho. A great feature with that data catalog is that Iota Ho will also profile your data as it creates the catalog, assigning some meaning to those pesky column underscore ones and custom variable underscore tents. They're always such a joy to deal with. Now, by leveraging this interface, we can start to answer the first part of your question and understand where the core relationships within our data exists. Uh, personally, I'm a big fan of this view, as it really just helps the i b naturally John to these focal points that coincide with these key columns following that train of thought, Let's examine the customer I D column that seems to be at the center of a lot of these relationships. We can see that it's a fairly important column as it's maintaining the relationship between at least three other tables. >>Now you >>notice all the connectors are in this blue color. This means that their system defined relationships. But I hope Tahoe goes that extra mile and actually creates thes orange colored connectors as well. These air ones that are machine learning algorithms have predicted to be relationships on. You can leverage to try and make new and powerful relationships within your data. >>Eso So this is really cool, and I can see how this could be leverage quickly now. What if I added new data sources or your multiple data sources and need toe identify what data sensitive can iota who detect that? >>Yeah, definitely. Within the hotel platform. There, already over 300 pre defined policies such as hip for C, C, P. A and the like one can choose which of these policies to run against their data along for flexibility and efficiency and running the policies that affect organization. >>Okay, so so 300 is an exceptional number. I'll give you that. But what about internal policies that apply to my organization? Is there any ability for me to write custom policies? >>Yeah, that's no issue. And it's something that clients leverage fairly often to utilize this function when simply has to write a rejects that our team has helped many deploy. After that, the custom policy is stored for future use to profile sensitive data. One then selects the data sources they're interested in and select the policies that meet your particular needs. The interface will automatically take your data according to the policies of detects, after which you can review the discoveries confirming or rejecting the tagging. All of these insights are easily exported through the interface. Someone can work these into the action items within your project management systems, and I think this lends to the collaboration as a team can work through the discovery simultaneously, and as each item is confirmed or rejected, they can see it ni instantaneously. All this translates to a confidence that with iota hope, you can be sure you're in compliance. >>So I'm glad you mentioned compliance because that's extremely important to my organization. So what you're saying when I use the eye a Tahoe automated platform, we'd be 90% more compliant that before were other than if you were going to be using a human. >>Yeah, definitely the collaboration and documentation that the Iot Tahoe interface lends itself to really help you build that confidence that your compliance is sound. >>So we're planning a migration. Andi, I have a set of reports I need to migrate. But what I need to know is, uh well, what what data sources? Those report those reports are dependent on. And what's feeding those tables? >>Yeah, it's a fantastic questions to be toe identifying critical data elements, and the interdependencies within the various databases could be a time consuming but vital process and the migration initiative. Luckily, Iota Ho does have an answer, and again, it's presented in a very visual format. >>Eso So what I'm looking at here is my entire day landscape. >>Yes, exactly. >>Let's say I add another data source. I can still see that unified 3 60 view. >>Yeah, One future that is particularly helpful is the ability to add data sources after the data lineage. Discovery has finished alone for the flexibility and scope necessary for any data migration project. If you only need need to select a few databases or your entirety, this service will provide the answers. You're looking for things. Visual representation of the connectivity makes the identification of critical data elements a simple matter. The connections air driven by both system defined flows as well as those predicted by our algorithms, the confidence of which, uh, can actually be customized to make sure that they're meeting the needs of the initiative that you have in place. This also provides tabular output in case you needed for your own internal documentation or for your action items, which we can see right here. Uh, in this interface, you can actually also confirm or deny the pair rejection the pair directions, allowing to make sure that the data is as accurate as possible. Does that help with your data lineage needs? >>Definitely. So So, Pat, My next big question here is So now I know a little bit about my data. How do I know I can trust >>it? So >>what I'm interested in knowing, really is is it in a fit state for me to use it? Is it accurate? Does it conform to the right format? >>Yeah, that's a great question. And I think that is a pain point felt across the board, be it by data practitioners or data consumers alike. Another service that I owe Tahoe provides is the ability to write custom data quality rules and understand how well the data pertains to these rules. This dashboard gives a unified view of the strength of these rules, and your dad is overall quality. >>Okay, so Pat s o on on the accuracy scores there. So if my marketing team needs to run, a campaign can read dependent those accuracy scores to know what what tables have quality data to use for our marketing campaign. >>Yeah, this view would allow you to understand your overall accuracy as well as dive into the minutia to see which data elements are of the highest quality. So for that marketing campaign, if you need everything in a strong form, you'll be able to see very quickly with these high level numbers. But if you're only dependent on a few columns to get that information out the door, you can find that within this view, eso >>you >>no longer have to rely on reports about reports, but instead just come to this one platform to help drive conversations between stakeholders and data practitioners. >>So I get now the value of IATA who brings by automatically capturing all those technical metadata from sources. But how do we match that with the business glossary? >>Yeah, within the same data quality service that we just reviewed, one can actually add business rules detailing the definitions and the business domains that these fall into. What's more is that the data quality rules were just looking at can then be tied into these definitions. Allowing insight into the strength of these business rules is this service that empowers stakeholders across the business to be involved with the data life cycle and take ownership over the rules that fall within their domain. >>Okay, >>so those custom rules can I apply that across data sources? >>Yeah, you could bring in as many data sources as you need, so long as you could tie them to that unified definition. >>Okay, great. Thanks so much bad. And we just want to quickly say to everyone working in data, we understand your pain, so please feel free to reach out to us. we are Website the chapel. Oh, Arlington. And let's get a conversation started on how iota Who can help you guys automate all those manual task to help save you time and money. Thank you. Thank >>you. Your Honor, >>if I could ask you one quick question, how do you advise customers? You just walk in this great example this banking example that you instantly to talk through. How do you advise customers get started? >>Yeah, I think the number one thing that customers could do to get started with our platform is to just run the tag discovery and build up that data catalog. It lends itself very quickly to the other needs you might have, such as thes quality rules. A swell is identifying those kind of tricky columns that might exist in your data. Those custom variable underscore tens I mentioned before >>last questions to be to anything to add to what Pat just described as a starting place. >>I'm no, I think actually passed something that pretty well, I mean, just just by automating all those manual task. I mean, it definitely can save your company a lot of time and money, so we we encourage you just reach out to us. Let's get that conversation >>started. Excellent. So, Pete and Pat, thank you so much. We hope you have learned a lot from these folks about how to get to know your data. Make sure that it's quality, something you can maximize the value of it. Thanks >>for watching. Thanks again, Lisa, for that very insightful and useful deep dive into the world of adaptive data governance with Iota Ho Oracle First Bank of Nigeria This is Dave a lot You won't wanna mess Iota, whose fifth episode in the data automation Siri's in that we'll talk to experts from Red Hat and Happiest Minds about their best practices for managing data across hybrid cloud Inter Cloud multi Cloud I T environment So market calendar for Wednesday, January 27th That's Episode five. You're watching the Cube Global Leader digital event technique

>> Hello everybody and welcome back to the cubes coverage of the Snowflake Data Cloud Summit 2020. We're tracking the rise of the Data Cloud, and fresh off the keynotes here, Frank Slootman, the chairman and CEO of Snowflake and Anita Lynch, the vice president of Data Governance at Disney Streaming Services. Folks, welcome. >> Thank you Thanks for having us, Dave, >> I need a Disney plus awesome. You know, we signed up early, watched all the Marvel movies, Hamilton, the new Pixar Movie saw, I haven't gotten into the Mandalorian yet, your favorite, but, (woman laughing) really appreciate you guys coming on. Let me start with Frank. I'm glad you're putting forth this vision around the data cloud, because I never liked the term Enterprise Data Warehouse, what you're doing is so different from the sort of that legacy world that I've known all these years, but start with why the Data Cloud, what problems are you trying to solve? And maybe some of the harder challenges you're seeing. >> Yeah I know, you know we've come a long way in terms of workload execution, right? In terms of scale and performance, you know, concurrent execution, we really taken the lid off sort of the physical constraints that have existed on these types of operations. But there's one problem, that we're not yet solving. And that is the siloing and bunkering of data. And especially in a data is locked in application it is locked data centers, is locked in cloud regions, incredibly hard for data science teams to really, you know, unlock the true value of data. When you can address patterns that exist across a data set. So we're perpetuate, a status we've had forever since the beginning of computing. If we don't start to crack that problem now we have that opportunity. But the notion of a Data Cloud is like basically saying, look folks, you know, we have to start unsiloing and unlocking the data, and bring it into a place, you know, where we can access it, you know, across all these parameters and boundaries that have historically existed. It's very much a step level function. Now the customers have always looked at things one workload at a time, that mentality really has to go. You really have to have a Data Cloud mentality, as well as a workload orientation towards managing data. >> Anita was great hearing your role at Disney, and your keynote, and the work you're doing, the governance work, and you're serving a great number of stakeholders, enabling things like data sharing, you got really laser focused on trust, compliance, privacy. Is this idea of a data clean room is really interesting. You maybe you can expand on some of these initiatives here and share what you're seeing as some of the biggest challenges to success. And of course, the opportunities that you're unlocking. >> Sure. I mean, in my role, leading Data Governance, it's really critical to make sure that all of our stakeholders, not only know what data is available and accessible to them, they can also understand really easily and quickly, whether or not the data that they're using is for the appropriate use case. And so, that's a big part of how we scale data governance, and a lot of the work that we would normally have to do manually, is actually done for us through the data clean rooms. >> Thank you for that. I wonder if you could talk a little bit more about the role of data and how your data strategy has evolved and maybe discuss some of the things that Frank mentioned about data silos, and obviously you can relate to that, having been in the data business for awhile, but wonder if you can elucidate on that. >> Sure. I mean, data complexities are going to evolve over time in any traditional data architecture, simply because you often have different teams at different periods in time, trying to analyze and gather data across a whole lot of different sources. And the complexity that just arises out of that, is, due to the different needs of specific stakeholders. There are time constraints, and quite often it's not always clear, how much value they're going to be able to extract from the data at the outset. So what we've tried to do to help break down those silos, is, allow individuals to see upfront how much value they're going to get from the data, by knowing that it's trustworthy right away . By knowing that it's something that they can use in their specific use case right away. And by ensuring that essentially, as they're continuing to kind of scale the use cases that they're focused on, they're no longer required to make multiple copies of the data, do multiple steps to reprocess the data. And that makes all the difference in the world. >> Yeah, for sure. I mean copy creek, cause it'd be the silent killer. Frank I followed you for a number of years. You're a big thinker. You and I have had a lot of conversations about the near term, mid term and long term. I wonder if you could talk about, you know, when your keynote, you talked about eliminating silos, and connecting across data sources, which really powerful concept, but it really only, if people are willing and able to connect and collaborate, where do you see that happening? Maybe what are some of the blockers there? >> Well, there's certainly a natural friction there. I still remember when we first started to talk to Salesforce, you know, they had, discovered that we were a top three destination of Salesforce data and they were wondering, you know, why that was? And the reason is of course that people take Salesforce data, push it to Snowflake, because they want to overlay it with data outside of Salesforce. You know what it is Adobe or any 6other marketing dataset. And then they want to run very highly skilled processes, you know, on it. But the reflexes in the world of SAS, is always like, no, we're an Island, we're a planet onto ourselves. Everybody needs to come with us as opposed to, we go to a different platform to run these types of processes. It's no different for thee public cloud vendor. They did only, they have, you know, massive moats around, you know, their storage to, you know, to really prevent data from leaving their orbit. So there is natural friction in terms of for this to happen. But on the other hand, you know, there is an enormous need, you know, we can't deliver on the power and potential of data, unless we allow it to come together. Snowflake is the platform that allows that to happen. You know, we were pleased with our relationship with Salesforce because they did appreciate, you know, why this was important and why this was necessary. And we think, you know, other parts of the industry will gradually come around to it as well. So the idea of a Data Cloud has really come. Right, people are recognizing, you know, why does this matters now. It's not going to happen overnight. It is a step what will function a very big change in mentality and orientation. You know? >> Yeah. It's almost as though the sussification of our industry sort of repeated some of the application silos, and build a heart on to and all the processes of(mummers) Okay, here we go. And you're really trying to break that aren't you? >> Yep, exactly. >> Anita, again, I want to come back to this notion of governance. It's so important. It's the first rule in your title, and it really underscores the importance of this. You know, Frank was just talking about some of the hurdles and this is a big one. I mean we saw this in the early days of big data where governance was this afterthought. It was like bolted on kind of wild west. I'm interested in your governance journey. And maybe you can share a little bit about what role Snowflake has played there in terms of supporting that agenda, and kind of what's next on that journey. >> Sure. Well, you know, I've led data teams, in numerous ways over my career, this is the first time, that I've actually had the opportunity to focus on governance. And what it's done, is allowed for my organization to scale much more rapidly. And that's so critically important for our overall strategy as a company. >> Well, I mean, a big part of what you were talking about, at least my inference in your talk, was really that the business folks didn't have to care about, your wonder about they cared about it, but they don't have to wonder about, and about the privacy concerns, et cetera, you've taken care of all that. It's sort of transparent to them. >> Yeah, that's right. Absolutely. So we focus on ensuring compliance across all of the different regions where we operate. We also partner very heavily with our legal and information security teams. They're critical, to ensuring you know, that we're able to do this. We don't do it alone. But governance includes not just, you know, the compliance and the privacy. It's also about data access. And it's also about ensuring data quality. And so all of that comes together under the governance umbrella. I also lead teams that focus on things like instrumentation, which is how we collect data, we focus on the infrastructure, and making sure that we've architected for scale , and all of these are really important components of our strategy. >> So I have a question maybe each of you can answer it. I sort of see this, our industry moving from products to then, to the platforms and platforms even evolving into ecosystems. And then there's this ecosystem of data. You guys both talked a lot about data sharing, but, but maybe Frank, you can start and Anita you can add onto Frank's answer. You obviously both passionate about the use of data and trying to do so in a responsible way, that's critical, but it's also going to have business impact. Frank, where's this passion come from on your side? And how are you putting into action in your own organization? >> Well, you know, I'm really going to date myself here, but you know many years ago, you know, I saw the first glimpse of multidimensional databases that were used for reporting really on IBM mainframes. And it was extraordinarily difficult. We didn't even have the words back then in terms of data, warehouses and business, all these terms didn't exist. People just knew that they want to have a more flexible way of reporting and being able to pivot data, dimensionally, all these kinds of things. And I just by whatever this predates, you know, windows 3.1, which really, you know, set off the whole sort of graphical, you know, way of dealing with systems, which there's not whole generations of people that don't know any different. Right? So I've lived the pain of this problem, and sort of had a front row seat, to watching this transpire over a very long period of time. And that's one of the reasons, you know, why I'm here because I finally seen, you know, a glimpse of, you know, I also, as an industry fully, just unleashing and unlocking the potential. We're not at a place where the technology is ahead of people's ability to harness it. Right. Which we'd never been there before. Right. It was always like we wanted to do things and technology wouldn't let us, it's different now. I mean, people are just, heads are spinning with what's now possible, which is why you see Marcus evolve, you know, very rapidly right now, we were talking earlier about how you can't take, you know, past definitions and concepts and apply them to what's going on in the world. The world's changing right in front of your eyes right now, >> So Anita maybe you could add on to what Frank just said and share some of the business impacts, and outcomes that are notable since you've really applied your love of data and maybe touch culture, data culture. Any words of wisdom for folks in the audience who might be thinking about embarking on a Data Cloud journey, similar to what you've been on. >> Yeah Sure. I think for me, I fell in love with technology first, and then I fell in love with data and I fell in love with data because of the impact that data can have, on both the business, and the technology strategy. And so it's sort of that nexus between all three. And in terms of my career journey and some of the impacts that I've seen. I think with the advent of the Cloud, you know, before, well, how do I say this? Before the cloud actually became so prevalent and such a common part of the strategy that's required, it was so difficult, you know, so painful. It took so many hours to actually, be able to calculate, you know, the volumes of data that we had. Now we have that accessibility. And then on top of it, with the Snowflake Data Cloud, it's much more performance oriented from a cost perspective because you don't have multiple copies of the data, or at least you don't have to have multiple copies of the data. And I think, moving beyond some of the traditional mechanisms for measuring business impact, has only been possible with the volumes of data that we have available to us today. And it's just, it's phenomenal to see the speed at which we can operate and really, truly understand our customer's interests and their preferences, and then tailor the experiences that they really want and deserve for them. It's been a great feeling to get to this point in time. >> That's fantastic. So, Frank, I got to ask you to do so in your spare time you decided to write a book am loving it. I have a signed copy, so I'm going to have to send it back and have you sign it. But, and I love the inside baseball. It's just awesome. So really appreciate that. So, but why did you decide to write a book? >> Well, there were a couple of reasons. Obviously we thought it was an interesting tale to tell for anybody, you know, who is interested in, you know, what's going on? How did this come about? You know, or the characters behind the scenes and all this kind of stuff. But, you know, from a business standpoint, you know, because this is such a step function, it's so non incremental, we felt like we really needed quite a bit of real estate to really lay out, what the full narrative and context is. And, you know, we thought, you know, books titled the rise of the Data Cloud. That's exactly what it is. And we're trying to make the case for that mindset, that mentality, that strategy, because all of us, you know, I think as an industry we're at risk of, you know, persisting, perpetuating, you know, where we've been since the beginning of computing. So we're really trying to make a pretty forceful case for look, you know, there's an enormous opportunity out there, but there's some choices you have to make along the way. >> Guys, we got to leave it there. Frank. I know you and I are going to talk again, Anita, I hope we have a chance to meet face to face and in the cube live someday, your phenomenal guest and what a great story. Thank you both for coming on. Thanks Dave, >> Thank you >> You're welcome to keep it right there, buddy. We'll be back with the next guest right after this short break. (upbeat music)

>> From theCUBE studios in Palo Alto in Boston, connecting with thought leaders all around the world, this a CUBE conversation. >> Hello, and welcome to this CUBE conversation, I'm John Furrier, host of theCUBE here, in our Palo Alto, California studios, here with our quarantine crew. We're getting all the remote interviews during this time of COVID-19. We've got two great remote guests here, Rachini Moosavi who's the Executive Director of Analytical Services and Data Governance at UNC Healthcare, and Sonya Jordan, Enterprise Analytics Manager of Data Governance at UNC Health. Welcome to theCUBE, thanks for coming on. >> Thank you. >> Thanks for having us. >> So, I'm super excited. University of North Carolina, my daughter will be a freshman this year, and she is coming, so hopefully she won't have to visit UNC Health, but looking forward to having more visits down there, it's a great place. So, thanks for coming on, really appreciate it. Okay, so the conversation today is going to be about how data and how analytics are helping solve problems, and ultimately, in your case, serve the community, and this is a super important conversation. So, before we get started, talk about UNC Health, what's going on there, how you guys organize, how big is it, what are some of the challenges that you have? >> SO UNC Health is comprised of about 12 different entities within our hospital system. We have physician groups as well as hospitals, and we serve, we're spread throughout all of North Carolina, and so we serve the patients of North Carolina, and that is our primary focus and responsibility for our mission. As part of the offices Sonya and I are in, we are in the Enterprise Analytics and Data Sciences Office that serves all of those entities and so we are centrally located in the triangle area of North Carolina, which is pretty central to the state, and we serve all of our entities equally from our Analytics and Data Governance needs. >> John: You guys got a different customer base, obviously you've got the clinical support, and you got the business applications, you got to be agile, that's what it's all about today, you don't need to rely on IT support. How do you guys do that? What's the framework? How do you guys tackle that problem of being agile, having the data be available, and you got two different customers, you got all the compliance issues with clinical, I can only imagine all the regulations involved, and you've got the business applications. How do you handle those? >> Yeah, so for us in the roles that we are in, we are fully responsible for more of the data and analytics needs of the organization, and so we provide services that truly are balanced across our clinician group, so we have physicians, and nurses, and all of the other ancillary clinical staff that we support, as well as the operational needs as well, so revenue cycle, finance, pharmacy, any of those groups that are required in order to run a healthcare system. So, we balance our time amongst all of those and for the work that we take on and how we continuously support them is really based on governance at the end of the day. How we make decisions around what the priorities are and what needs to happen next, and requires the best insights, is really how we focus on what work we do next. As for the applications that we build, in our office, we truly only build analytical applications or products like visualizations within Tableau as well as we support data governance platforms and services and so we provide some of the tools that enable our end users to be able to interact with the information that we're providing around analytics and insights, at the end of the day. >> Sonya, what's your job? Your title is Analytics Manager of Data Governance, obviously that sounds broad but governance is obviously required in all things. What is your job, what is your day-to-day roles like? What's your focus? >> Well, my day-to-day operations is first around building a data governance program. I try to work with identifying customers who we can start partnering with so that we can start getting documentation and utilizing a lot of the programs that we currently have, such as certification, so when we talk about initiatives, this is one of the initiatives that we use to partner with our stakeholders in order to start bringing visibilities to the various assets, such as metrics, or universes that we want to certify, or dashboards, algorithm, just various lists of different types of assets that we certify that we like to partner with the customers in order for them to start documenting within the tools, so that we can bring visibility to what's available, really focusing on data literacy, helping people to understand what assets are available, not only what assets are available, but who owns them, and who own the asset, and what can they do with it, making sure that we have great documentation in order to be able to leverage literacy as well. >> So, I can only imagine with how much volume you guys are dealing from a data standpoint, and the diversity, that the data warehouse must be massive, or it must be architected in a way that it can be agile because the needs, of the diverse needs. Can you guys share your thoughts on how you guys look on the data warehouse challenge and opportunity, and what you guys are currently doing? >> Well, so- >> Yeah you go ahead, Rachini. >> Go ahead, Sonya. >> Well, last year we implemented a tool, an enterprise warehouse, basically behind a tool that we implemented, and that was an opportunity for Data Governance to really lay some foundation and really bring visibility to the work that we could provide for the enterprise. We were able to embed into probably about six or seven of the 13 initiatives, I was actually within that project, and with that we were able to develop our stewardship committee, our data governance council, and because Rachini managed Data Solutions, our data solution manager was able to really help with the architect and integration of the tools. >> Rachini, your thoughts on running the data warehouse, because you've got to have flexibility for new types of data sources. How do you look at that? >> So, as Sonya just mentioned, we upgraded our data warehouse platform just recently because of these evolving needs, and like a lot of healthcare providers out there, a lot of them are either one or the other EMRs that are top in the market. With our EMR, they provide their own data warehouse, so you have to factor almost the impact of what they bring to the table in with an addition to all of those other sources of data that you're trying to co-mingle and bring together into the same data warehouse, and so for us, it was time for us to evolve our data warehouse. We ended up deciding on trying to create a virtual data warehouse, and in doing so, with virtualization, we had to upgrade our platform, which is what created that opportunity that Sonya was mentioning. And by moving to this new platform we are now able to bring all of that into one space and it's enabled us to think about how does the community of analysts interact with the data? How do we make that available to them in a secure way? In a way that they can take advantage of reusable master data files that could be our source of truth within our data warehouse, while also being able to have the flexibility to build what they need in their own functional spaces so that they can get the wealth of information that they need out of the same source and it's available to everyone. >> Okay, so I got to ask the question, and I was trying to get the good stuff out first, but let's get at the reality of COVID-19. You got pre-COVID-19 pandemic, we're kind of in the middle of it, and people are looking at strategies to come out of it, obviously the world will be changed, higher with a lot of virtualization, virtual meetings, and virtual workforce, but the data still needs to be, the business still needs to run, but data will be changing different sources, how are you guys responding to that crisis because you're going to be leaned on heavily for more and more support? >> Yeah it's been non-stop since March (laughs). So, I'm going to tell you about the reporting aspects of it, and then I'd love to turn it over to Sonya to tell you about some of the great things that we've actually been able to do to it and enhance our data governance program by not wasting this terrible event and this opportunity that's come up. So, with COVID, when it kicked off back in March, we actually formed a war room to address the needs around reporting analytics and just insights that our executives needed, and so in doing so, we created within the first week, our first weekend actually, our first dashboard, and within the next two weeks we had about eight or nine other dashboards that were available. And we continuously add to that. Information is so critical to our executives, to our clinicians, to be able to know how to address the evolving needs of COVID-19 and how we need to respond. We literally, and I'm not even exaggerating, at this very moment we have probably, let's see, I think it's seven different forecasts that we're trying to build all at the same time to try and help us prepare for this new recovery, this sort of ramp up efforts, so to your point, it started off as we're shutting down so that we can flatten the curve, but now as we try to also reopen at the same time while we're still meeting the needs of our COVID patients, there's this balancing act that we're trying to keep up with and so analytics is playing a critical factor in doing that. >> Sonya, your thoughts. First of all, congratulations, and action is what defines the players from the pretenders in my mind, you're seeing that play out, so congratulations for taking great action, I know you're working hard. Sonya, your thoughts, COVID, it's putting a lot of pressure? It highlights the weaknesses and strengths of what's kind of out there, what's your thoughts? >> Well, it just requires a great deal of collaboration and making sure that you're documenting metrics in a way where you're factoring true definition because at the end of the day, this information can go into a dashboard that's going to be visualized across the organization, I think what COVID has done was really enhanced the need and the understanding of why data governance is important and also it has allowed us to create a lot of standardization, where we we're standardizing a lot of processes that we currently had in correct place but just enhancing them. >> You know, not to go on a tangent, but I will, it's funny how the reality has kind of pulled back, exposed a lot of things, whether it's the remote work situation, people are VPNing, not under provision with the IT side. On the data side, everyone now understands the quality of the data. I mean, I got my kids talking progression analysis, "Oh, the curves are all wrong," I mean people are now seeing the science behind the data and they're looking at graphs all the time, you guys are in the visualization piece, this really highlights the need of data as a story, because there's an impact, and two, quality data. And if you don't have the data, the story isn't being told and then misinformation comes out of it, and this is actually playing out in real time, so it's not like it's just a use case for the most analytics but this again highlights the value of proposition of what you guys do. What's your personal thoughts on all this because this really is playing out globally. >> Yeah, it's been amazing how much information is out there. So, we have been extremely blessed at times but also burdened at times by that amount of information. So, there's the data that's going through our healthcare system that we're trying to manage and wrangle and do that data storytelling so that people can drive those insights to very effective decisions. But there's also all of this external data that we're trying to be able to leverage as well. And this is where the whole sharing of information can sometimes become really hard to try and get ahead of, we leverage the Johns Hopkins data for some time, but even that, too, can have some hiccups in terms of what's available. We try to use our State Department of Health and Human Services data and they just about updated their website and how information was being shared every other week and it was making it impossible for us to ingest that into our dashboards that we were providing, and so there's really great opportunities but also risks in some of the information that we're pulling. >> Sonya, what's your thoughts? I was just having a conversation this morning with the Chief of Analytics and Insight from NOA which is the National Oceanic Administration, about weather data and forecasting weather, and they've got this community model where they're trying to get the edges to kind of come in, this teases out a template. You guys have multiple locations. As you get more democratized in the connection points, whether it's third-party data, having a system managing that is hard, and again, this is a new trend that's emerging, this community connection points, where I think you guys might also might be a template, and your multiple locations, what's your general thoughts on that because the data's coming in, it's now connected in, whether it's first-party to the healthcare system or third-party. >> Yeah, well we have been leveraging our data governance tool to try to get that centralized location, making sure that we obtain the documentations. Due to COVID, everything is moving very fast, so it requires us to really sit down and capture the information and when you don't have enough resources in order to do that, it's easy to miss some very important information, so really trying to encourage people to understand the reason why we have data governance tools in order for them to leverage, in order to capture the documentation in a way that it can tell the story about the data, but most of all, to be able to capture it in a way so that if that person happened to leave the organization, we're not spending a lot of time trying to figure out how was this information created, how was this dashboard designed, where are the requirements, where are the specifications, where are the key elements, where does that information live, and making sure we capture that up front. >> So, guys, you guys are using Informatica, how are they helping you? Obviously, they have a system they're getting some great feedback on, how are you using Informatica, how is it going, and how has that enabled you guys to be successful? >> Yeah, so we decided on Informatica after doing a really thorough vetting of all of the other vendors in the industry that could provide us these services. We've really loved the capabilities that we've been able to provide to our customers at this point. It's evolving, I think, for us, the ability to partner with a group like Prominence, to be able to really leverage the capabilities of Informatica and then be really super, super hyper focused on providing data literacy back to our end users and making that the full intent of what we're doing within data governance has really enabled us to take the tools and make it something that's specific to UNC Health and the needs that our end users are verbalizing and provide that to them in a very positive way. >> Sonya, they talk about this master catalog, and I've talked to the CEO of Informatica and all their leaders, governance is a big part of it, and I've always said, I've always kind of had a hard time, I'm an entrepreneur, I like to innovate, move fast, break things, which is kind of not the way you work in the data world, you don't want to be breaking anything, so how do you balance governance and compliance with innovation? This has been a key topic and I know that you guys are using their enterprise data catolog. Is that helping? How does that fit in, is that part of it? >> Well, yeah, so during our COVID initiatives and building these telos dashboards, these visualizations and forecast models for executive leaders, we were able to document and EMPower you, which we rebranded Axon to EMPower, we were able to document a lot of our dashboards, which is a data set, and pretty much document attributes and show lineage from EMPower to EDC, so that users would know exactly when they start looking at the visualization not only what does this information mean, but they're also able to see what other sources that that information impacts as well as the data lineage, where did the information come from in EDC. >> So I got to ask the question to kind of wrap things up, has Informatica helped you guys out now that you're in this crisis? Obviously you've implemented before, now that you're in the middle of it, have you seen any things that jumped out at you that's been helpful, and are there areas that need to be worked on so that you guys continue to fight the good fight, come out of this thing stronger than before you came in? >> Yeah, there is a lot of new information, what we consider as "aha" moments that we've been learning about, and how EMPower, yes there's definitely a learning curve because we implemented EDC and EMPower last year doing our warehouse implementation, and so there's a lot of work that still needs to be done, but based on where we were the first of the year, I can say we have evolved tremendously due to a lot of the pandemic issues that arised, and we're looking to really evolve even greater, and pilot across the entire organization so that they can start leveraging these tools for their needs. >> Rachini you got any thoughts on your end on what's worked, what you see improvements coming, anything to share? >> Yeah, so we're excited about some of the new capabilities like the marketplace for example that's available in Axon, we're looking forward to being able to take advantage of some of these great new aspects of the tool so that we can really focus more on providing those insights back to our end users. I think for us, during COVID, it's really been about how do we take advantage of the immediate needs that are surfacing. How do we build all of these dashboards in record-breaking time but also make sure that folks understand exactly what's being represented within those dashboards, and so being able to provide that through our Informatica tools and service it back to our end users, almost in a seamless way like it's built into our dashboards, has been a really critical factor for us, and feeling like we can provide that level of transparency, and so I think that's where as we evolve that we would look for more opportunities, too. How do we make it simple for people to get that immediate answers to their questions, of what does the information need without it feeling like they're going elsewhere for the information. >> Rachini, thank you so much for your insight, Sonya as well, thanks for the insight, and stay safe. Sonya, behind you, I was pointing out, that's your artwork, you painted that picture. >> Yes. >> Looks beautiful. >> Yes, I did. >> You got two jobs, you're an artist, and you're doing data governance. >> Yes, I am, and I enjoy painting, that's how I relax (laughs). >> Looks great, get that on the market soon, get that on the marketplace, let's get that going. Appreciate the time, thank you so much for the insights, and stay safe and again, congratulations on the hard work you're doing, I know there's still a lot more to do, thanks for your time, appreciate it. >> Thank you. >> Thank you. >> It's theCUBE conversation, I'm John Furrier at the Palo Alto studios, for the remote interviews with Informatica, I'm John Furrier, thanks for watching. (upbeat music)

>> Presenter: From theCUBE Studios in Palo Alto and Boston, connecting with Dot leaders all around the world. This is a CUBE conversation. >> Everyone welcome to theCUBE studio here in Palo Alto. I'm John Furrier, host of theCUBE. We're here with our quarantine crew. We've been here for three months quarantining but we're getting the stories out. We're talking to all of our favorite guests and most important stories in technologies here remotely and we have a great conversation in store for you today with Amit Walia CEO of Informatica. Cube alumni, frequent guest of theCUBE, now, the CEO of Informatica. Amit, great to see you. Thank you for coming on this CUBE conversation. >> Good to see you John. It's different to be doing this like this versus being in the studio with you but I'm glad that we could leverage technology to still talk to each other. >> You're usually right here, right next to me, but I'm glad to get you remotely at least and I really appreciate you. You always have some great commentary and insights. And Amit, before we get into the real meaty stuff that I'd love around the data, I want to get your thoughts on this COVID-19 crisis. It's a new reality, it's highlighted as we've been reporting on SiliconANGLE for the past few months. The at scale problems that people are facing but it's also an opportunity. People are sheltered in place, there's a lot of anxiety on what their work environment is going to look like but the world still runs. Your thoughts on the current crisis and how you're looking at it, how you're navigating it as a leader. >> No doubt, it is a very unique situation we all live in. We've never all faced something like this. So I think first of all, I'll begin by expressing my prayers for anyone out there who has been impacted by it and of course, a huge round of thank you to all the heroes out there at the front lines. The healthcare workers, the doctors, the nurses (mumbles) so we can't forget that. These are very unique situations but as you said, let's not forget that this is a health crisis first and then it becomes an economic crisis. And then, as you said there is a tremendous amount of disruption and (mumbles) I think all of them will go through some phases and I think you can see already while there is disruption in front of us, you see the digital contents of organizations who are ready for that have definitely faced it lot better but as obviously the ones that have been somewhat in the previous generations, let's just say business models or technologies models are struggling through it. So there is a lot data chain. I think they're still learning. We're absolutely still learning and we will continue to learn til the end of this year and we'll come out very different for the next decade for sure. >> If anyone who's watching goes to YouTube on the SiliconANGLE CUBE and look at your videos over the years, we've been talking about big data and these transformational things. It's been an inside the industry kind of discussion. Board room for your clients and your business and Informatica but I think this is now showing the world this digital transformation. The future has been pulled forward faster than people have been expecting it and innovation strategy has been on paper, maybe some execution but now I think it's apparent to everyone that the innovation strategy needs to start now because of this business model impact, the economic crisis is exposed. The scale of opportunities and challenges, there will be winners and losers and projects still need to get done or reset or reinvented to come out of this with growth. So this is going to be the number one conversation. What are your thoughts around this? >> No, so I've talked to hundreds of customers across the globe and we see the same thing. In fact actually, in some ways as we went through this, something very profound dawned on me. We, John, talked about digital transformation for the last few years and clearly digital transformation will accelerate but as I was talking to customers, I came to this realization that we actually haven't digitally transformed. To be honest, what happened in the last three to four years is that it was more digital modernization. A few apps got tweaked, a few front-ends got tweaked but if you realize, it was more digital modernization, not transformation because in my opinion, there are four aspects to digital transformation. You think of new products and services, you think of new models of engaging with your customers, you think of absolutely new operating models and you think of fundamentally new business models. That's a whole rewrite of an organization, which is not just creating a new application out there, fundamental end to end transformation. My belief is, our belief is that, now starts a whole new era of transformation, digital transformation. We've just gone through digital modernization. >> Well, that's a great point and the business model impacts create... And in times of these inflection points, and again, you're a student of history in the tech industry, PC revolution, TCP IP. These are big points in time. They're not transitions. The big players tend to win the transitions. When you have a transformation, it's a Cambrian explosion of new kinds of capabilities. This is really, I would agree with your point but I think it's going to be a Cambrian explosion because the business model forcing function is there. How do you see it play, 'cause you're in the middle of all this, 'cause you guys are the control plane for data in the industry as a company. You enable these new apps. Could you share your-- >> So, we see a lot of that and I think the way to think about it, I think first of all, you said it right. This is a step function changing orbit. This is a whole new... You get to a new curve, you go to a different model. It's a whole new equation you're hiking for the curve you're going to be on. It's not just changing the gradient of the curve you've been on, this is going to be a whole journey. And when we think of the new world of digital transformation, there are four elements that are taught. First of all, it has to be strategic. It has to be Board, CEO, executive topped down, fundamentally across the whole organization, across every function of an organization. Second one you talked about scale. I believe this is all about innovating at scale. It's not about, hey, let me go put a new application in some far plans of my business. You've got to innovate at scale, end to end change does not happen in bits and pieces. Third one, this is cloud native, absolutely cloud native. If there was any minuscule of doubt, this is taking it away. Cloud nativity is the fundamental differentiator and the last but not the least is digital natives, which is where everybody wants to go become a digitally transformed company that are data-led. You got to make data-led decisions. So for competence, strategic mindset, innovation at scale cloud nativity and being data-led is going to define digital transformation. >> I think that encapsulates absolutely innovation strategy. I agree with you 100%, that's really insightful. I want to also get your thoughts on some things that you're talking about and you have always had some really kind of high level conversations around this and theCUBE has been a very social organization. We'd love to be that social construct between companies and audiences but you use a term, the digital transformation, the soul of digital transformation is data 4.0. This idea of having a soul is interesting because the apps all have personalization built in. You have CLAIRE, you've been doing CLAIRE AI for a while. So this idea of social organizations, a soul is kind of an interesting piece of metadata you're putting out in the messaging. What do you mean by that? How can digital transmission have a soul? >> I think we talked about it a lot and I think it just came to me that, look at the end of the day, any transformation is so fundamental to anything that anybody does and I think if you think about, you can go to a fundamental transformation that is just qualitative, it's qualitative and quantitative. It's about a human body, it's about a human body transforming itself and then something doesn't have a soul, John, it does not have life. It cannot truly move to the next paradigm. So I believe that, any transformation has to have a soul and the digital world is all about data. So obviously, we believe that we're walking into a data-for-data world where, as I said, the four pillars of digital transformation would be data-led and I believe data is the soul of that transformation and data itself is moving into a new paradigm. You've heard us talk about 1.0, 2.0, 3.0, and this is the new world of 4.0, a data 4.0 which basically is all about cloud nativity, intelligent automation, AI powered, focusing on data, trust in data ethics and operations and innovation at scale. When you bring these elements together, then that enables digital transformation to happen on the shoulders of data 4.0, which in my opinion, is the soul of digital transformation. >> All right, so just rewind on data 4.0 for a minute. Pretend I'm a CIO, I'm super busy. I don't have time to read up about it. Give me the bottom line, what is data 4.0? Describe it to me in basic terms, is it just an advancement, acceleration? What's the quick elevator pitch on 4.0, data 4.0? >> Very simple?. We're all walking into a world where we're going to be digital. Digital means that we're basically going to be creating tons of data. By the way, and data is everywhere. It's not just within the four walls of us. It's basically what I call transaction and interaction and with the scale and volume of data increasing, the complexity of it increasing. We want to make decisions. I say, tomorrow's decision, today and with data that is available to us yesterday, so I can be better at that decision. So we need intelligence, we need automation, we need flexibility, which is where AI comes in. These are all very fundamental rewrites of the technology stack to enable a fundamental business transformation. So in that world, data is front and center and you look at the amount of data we are going to collect, the whole concept of data ethics and data trust become very important, not just Goodwill governance, governance is important but data privacy, data trust becomes very important. Then we're going to do things like contact tracing, it's very important for the society but the ethics, trust and privacy of what you and I will give to the government is going to become very much important. So to me, that world that we go in, every enterprise has to think data first, data led, build an infrastructure to support the business in that context and then, as I said, then the soul, which is data will give life to digital transformation. >> That's awesome. Love the personalization and the soul angle on it. I always believe that you guys had that intelligent automation fabric and to me, you said earlier, cloud native is apparent to everyone now. I think out of all this crisis, I think the one thing that's not going to be debated anymore is that cloud native is the operating model. I think that's pretty much a done deal at this point. So having this horizontally scalable data, you know I've been on this rant for years. I think that's the killer app. I think having horizontally scalable data is going to enable a lot, souls and more life. So I got to ask you the real, the billion dollar question. I'm a customer of yours or prospect or a large enterprise. I'm seeing what's happening at scale, provisioning of VPNs for 100% employees at home, except for the most needed workers. I now see all the things I need to either process, I need to cancel and projects that double down on. I still got to go out and build my competitive advantage. I still have to run my business. So I need to really start deploying right out of the gate data centric, data first, virtual first, whatever you want to call it, the new reality first, this inflection point. What do I do? What is the things that you see as projects or playbook recipes that people could implement? >> First of all, we see a very fundamental reevaluation of the entire business model. In fact, we have this term that we're using now that we have to think of business has a business 360 and if I think about it in this new world, that the businesses that stood the test is that had basically what I call, a digital supply chain or in a very digital scalable way of interacting with their customers, being able to engage with their customers. A digital fabric often making sure that they can bring their product and services to the customers very quickly or in some cases, if they were creating new products and services, they had the ability for a whole new supply chain to reach that end customer. And of course, a business model that is flexible so they dont obviously, they can cater to the needs of their customers. So in all of these worlds, customers are a building digital, scalable data platforms and when I say platforms, it's not about some monolithic platform. These are, as you and I have talked about, very modular microservices based platform that reside on what we call metadata. Data has to be the soul of the digital enterprise. Metadata is the nervous system, that makes it all work. That's the left brain, right brain, that makes it all work, which is where we put AI on top. AI that works for the customers and then they leverage it but AI applied to that metadata allows them to be very flexible, nimble and make these decisions very rapidly, whether they are doing analytics for tomorrow's offering to be brought in front of a customer or understanding the customer better to give them something that appeals to them in changing times or to protect the customer's data or to provide governance on top of it. Anything that you would like to do has to ride on top of what I call a, AI led metadata driven platform that can scale horizontally. >> Okay, so I got to go to the next level on this, which is, okay, you got me on that. I hear what you're saying, I agree, great. But I got to put my developers to work and I got insight, I got analytics teams, I got competencies but Amit, my complexities don't go away. I still got compliance at scale, I got governance at scale but I also got, now my developers not just to get analytical insight, there's great dashboards and there's great analytic data out there, you guys do a good job there. I got to get my developers coding so I can get that agility of the data into the apps for visualization in the app or having a key ingredient of the software. How do I do that? What's your answer to that one? >> So, that's a critic use case. If you think about it, for a developer, one of the biggest challenge for analytics project is how do I bring all the data that is in sites across the enterprise so then I can put it in any kind of visualization analytics tool and things are happening at scale. An enterprise is spread across the globe. It's so many different data sources available everywhere. Again, what we've done is that as a part of the data platform when you focus upon the metadata, that allows you to go to one place where you can have full access to all of the data assets that are available across (mumbles). Do you remember at theCUBE years ago, we unveiled the launch of our enterprise data catalog, which as I said, was the Google for enterprise data through metadata. Now, developers don't have to go start wasting their time, trying to find whether data has (mumbles), through the catalog that CLAIRE is in-built, they have access to it. They can start putting that to work and figuring out how do I take different kinds of data? How do I put it in some data times tool? Through which we have the in-built integrations. Do what I call the valuable last mile work, which is where the intelligence is needed from them versus spend their energy trying to figure out where good data, clean data, all kinds of data sets. We have eliminated all of that complexity with the help of metadata data platform, CLAIRE, to let the developers do what I call value-added productive work. >> Amit, final question for you. I know you talk to customers a lot, you're always on the road, you got a great product background, that's where you came from, good mix understanding of the business but now your customers and prospects are trynna put the fires out. The big room that... No one's going to talk about their kitchen appliances when the house is burning down and in some cases on the business model side or if it's a growth strategy, they're going to put all their energies where the action is. So getting mind share with them is going to be very difficult. How are you as a leader and how is Informatica getting in front of these folks and saying, "Look, I know things are tough "but we're an important supplier for you." How do you differentiate? How are you going to get that mind share? What are some of those conversations? 'Cause this is really the psychology of the marketplace right now, the buyer and the customer. >> Well, first of all, obviously we had to adapt to reach our customers in a different way because, virtually based just like you and I are chatting right now and to be candid, our teams were fantastic in being able to do it. We've actually already had multiple pretty big sides of it. In fact, the first week before we started (mumbles), we had set up the MDM and Data Governance Summit up in New York and we expected thousands of customers to come there, ask them (mumbles) virtual and we did it virtually and we had three times more people attend the virtual event. It was much easier for people who attended from the confines of their living room. So we'd gone 100% virtual and good news is, that our customers are heavily engaged. We've actually had more participation of customers coming and attending our events. We've had obviously our customers speaking, talking about how they've created value. In light of that next week, we have the big event which we're calling, CLAIREview named after ClAIRE AI engine. It's basically a beautiful net-filled tech experience. We'll have a keynote, we'll have seasons and episodes, people can do bite-sized viewing at their own leisure. We'll talk about all kinds of transformation. In fact, we have Scott Guthrie who runs all of Azure and Cloud at Microsoft as a part of my Keynote. We have two great customers, CDO at XXL and a CEO of GDR nonprofit that does (mumbles) on diabetes work talk about the data journeys. We have Martin Byer from Gardner. So we've been able to pivot and our customers are heavily engaged because data is a P-zero or a P-one activity for them to invest in. So we haven't seen any drop-off in customer engagement with us and we've been very blessed that we have a very loyal and a very high retention rate customer base. >> Well, I would expect that being the center of the value proposition, where we've always said data has been. One more final question since this just popped in my head. You and I have been talking about the edge for years. Certainly now the edge is exposed, we all know what the edge is, it's working at home. It's the human, it's me, it's my IOT devices. More than ever, the edge is now the new perimeter. It's the edge and now the edges is there. There's something that you've been talking a while. This is another part of data fabric that's important. Your view on this new edge that's now visualized by everybody, realized this immersion. What's your thoughts on the edge? >> Oh, I think the edge is real now. You and me chatted about that almost four years ago and I (mumbles). Look, think of it this way. Think of how security is going to change. There's no more data center to which we route our traffic anymore. It's sitting over there somewhere where no human beings is going to have access. People are connecting to all kinds of cloud application directly from their offices or living rooms or their cultures and the world of security has to change in that context. And people are more going to be more, enterprise (mumbles) are more worried about, hey, how do I make sure that that data centric, privacy and security is there in my device and that connects to the third party cloud vendors versus I can't transfer traffic to mine, everything to my VPN. So the edge is going to become a lot more compute intensive as well as it will require a lot of the elements that are, to be honest, used to be data center centric. We have to lighten them and bring them to the edge so enterprises can feel assured and working because at the end of the day, they have to run a business by the standards that an enterprise is held to. So you will see a ton of innovation, by the way, robotics. Robotics is going to make edge even more interesting in live view. So I see the next couple of years, heavy IOP edge computing, just like the clients that are modeled to mainframe that the PC became like a mainframe in terms of compute capacity. I guarantee at the desktop, compute capacity will go down to the edge and we're going to see that happen in the next five years or so. >> The edge is the new data centers. I always say, it's the land is the way, the way is the land. Amit, great to see you and thanks for sharing and I'm sorry, we can't do it in person but this has been like a fireside chat meets CUBE interview, remote. Thanks for spending the time and sharing your insights and we've always had great interviews at your events, virtual again, this year. We're going to spread it out over time, good call. Thanks for coming on, I appreciate it. >> Thanks, John, take care. >> Okay, Amit, CEO of Informatica, always great to get the conversation updates from him on the industry and what Informatica, as at the center of the value proposition data 4.0. This is really the new transformation, not transition, data science, data, data engineering, all happening. theCUBE with our remote interviews, bringing you all the coverage here from our Palo Alto studios, I'm John Furrier. Thanks for watching. (gentle music)

>> Announcer: Live from Las Vegas, it's theCUBE! Covering Informatica World 2018. Brought to you by Informatica. >> Hello, everyone, welcome back. This is theCUBE's exclusive coverage of Informatica World 2018. It's our fourth year covering Informatica on the front lines. Every year it gets bigger and bigger. I'm John Furrier, the host of theCUBE, with Peter Burris, my co-host, with some, chief analyst at Wikibon and SiliconANGLE on theCUBE. Our next guest is theCUBE alumni Amit Walia, who's been on many times, even before he was president. Now he's the president of products and strategic ecosystems for Informatica. Great to see you, great to have you on. Congratulations on your keynote. Thanks for stopping by. >> Thanks, John, glad to be here. Always good to be back. >> You're super, well, I love talking with you because one, you know, the business is growing. You've been in the product side, you guys are all great product folks. And this, they're shipping products. It's not like it's, like, vaporware. It's, like, great stuff. Now Azure deal was announced. But now the timing of the data play with Switzerland, we talked about this fabric, better time than ever. This year, you got data lakes turned into data swamps last year. This year it's about governance and catalog. Good timing. What's your assessment? Give us your point of view from the keynote, timing, product. >> Well, I mean, I think you're exactly right. We see that it's a unique time, and it was building over the last couple of years. So, you know, we have this phrase that this is a data 3.0 world where data has become its own thing. It's no more captive to an application or a database. Those days are gone. And I think in data 3.0 world, I think we talked about it this morning in my keynote, that, you know, customers have to step back and think differently. You can't just do the same old things and expect to be different, and especially as they're driving digital transformations. So we introduced this concept of system thinking 3.0, where as you're thinking about a data, you have to think about it as a platform. A nimble platform, not a ERP-ish platform. Think of it at scale. It's doubling every year. >> Yeah. >> Think of it metadata in, metadata out. Let AI assist you. You know, you've got to have, we as humans are just going to be swamped with so much data we can't process it. And last, very important thesis, as you all know, is that governance, security, and privacy have to be design principles. They cannot be an afterthought. >> Last year you announced CLAIRE AI component of the system. >> Amit: Yeah. >> How has that evolved this year? I mean, I know it was a strategic centerpiece for you guys. Obviously the catalog is looking really strong right now, a lot of buzzing to show around the enterprise catalog. Where is the AI, the CLAIRE piece fitting in? Can you just give us the update on CLAIRE? >> Well, CLAIRE's come a long way. Basically part of every product we have. So it manifests itself probably most holistically in the catalog, but whether in the data lake, it's in the context of surfacing data, discovering data, giving recommendations of data to an analyst in a very business user context, all in the context of an MBM, giving you relationship discovery of, let's say, John, who you are, into who you are. So it is in Secure@Source helping anomaly detection happen. So CLAIRE has now made its way into every product. But as you said, the one product where it basically surfaces itself in its full bloom is the catalog, which, by the way, has been the fastest growing product in Informatica's history. One year since launch, it has just gone, taken off. >> Well, presumably there's a relationship. Sorry, John. Presumably there's relationship there. Catalogs have been around for years, but they've been very, very difficult to build and sustain and maintain. CLAIRE presumably is providing a capability that removes a lot of the drudgery associated with catalogs, and that's one of the things that's making it possible. Have I got that right? >> No, yeah, absolutely. And actually, building the new catalog also has been a hard thing. So in some ways building it for scale has been a massive common sense problem that we've been solving for the last three, four years. You know, collecting metadata across the full enterprise is a non-trivial activity, so it was never done across the enterprise ever. If you remember when I was here last time, our vision for the catalog was very simple. We want to be the Google for enterprise data... >> Peter: Yeah. >> ...through metadata. >> And that's what we were able to do through the catalog. But as you rightfully said, it's very hard to consume it if you don't write AI to help it. That's where CLAIRE made a very big road. So the UI's very straightforward. It's a Google UI, and any business user can, with the help of CLAIRE, start using it. >> But it persists. >> Yes. >> So unlike just putting a search term in and getting a page of stuff back, a catalog has to persist. >> Has a persistence, exactly. >> And so describe, now that you have that in place with CLAIRE, as John asked, where does it go? >> Solving use cases. Actually, I'll give you a little preview. Tomorrow I do the closing keynote, and usually what I do, the closing keynote is all about features. So actually, it's a whole demo on CLAIRE where we're taking CLAIRE to the whole next level. As a great example, you know, building data supply chains, you know, it's a manual activity that you have to do. With the help of the catalog, we actually understand the system architecture. So if you want to add new sources of data or change anything you want to do, you don't have to go through those steps again. We will service it to you and we'll tell you what to do. In fact, tomorrow I'll show what we call a self-integrating system. It'll happen by itself. You have to just go and say whether I agree or not agree and the machine learns. Next time it gets smarter and smarter. Or in the context of governance. If a new policy comes up in an enterprise, the biggest challenge is how do I even know what the impact of the new policy is? Look at GDPR right now. So with the help of CLAIRE, we can understand across the entire enterprise what would be the impact of that policy across different functions and what the gaps are. Those are the kind of places we are taking CLAIRE towards more bigger business-driven initiatives. In fact, tomorrow there'll be a whole demo on that one. >> I mean, GDPR is interesting because it really exposes who's ready. >> Yeah. >> Who has had invested their, the engineering in data, understands the data. So that's clear. We're seeing some, and it's also a shot across the bow of companies saying, look, you got to think strategically around your data. We talk about this all the time with you guys, so it's not new to us, but it is new to the fact that some people are right now sitting there going, oh no, I need to do something. >> Amit: Yeah. >> How is Informatica going to help me if I have a GDPR awakening of, oh man, I got to do something. >> You know, GDPR... >> Do I just call you up and do the, roll in the catalog? Do I... >> That's a great place to begin, by the way. So GDPR, by the way, is a data problem. So GDPR is not necessarily a compliance/security problem, because you want to understand which data pass through boundaries, who's accessing it. It's a true data problem. So today, I mean, in fact, at Informatica World, we have customers like PayPal talking about their journey with us on GDPR. And so you begin with the catalog, and then we have three products that help in the GDPR journey, the catalog, Secure@Source, and the Data Governance Axon product. And again, each company's GDPR implications are slightly different, and companies, as I said, like MasterCard, like PayPal, that are using our products to run their GDPR activity right now, it's a... So we are seeing that going through the roof. And in fact, one of the big use cases for catalog has been in the context of governance and GDPR. >> I want to talk about the trends on, that are impacting you guys. Again, I was saying earlier that it's a tailwind for you guys. The timing's perfect. Multi-cloud, hybrid cloud. I'd say hybrid cloud's probably in its second year, maybe third year hype, but now multi-cloud is real. You have announced a Azure relationship. You guys have a growing ecosystem opportunity. >> Amit: Yeah. >> How are you guys looking at it? 'Cause it's really emergent. It's happening right now. How are you guys targeting the ecosystem, whether it's business development partnerships, joint product development go to market, and/or on the business side? What's the orientation, what's the posture? Are you guy taking a certain approach, expecting certain growth? What's the update on the ecosystem, the global partner landscape? >> You know, the way we think about ourselves is that we've been the Switzerland of data always. And customers, actually, I always say it's always customer-backed. >> John: Yeah. >> If you solve for the customer, everything goes good. Customers expect us to do that. And customers are going to be in a heterogeneous world. Nobody's going to ever pick one stack. You know, you all know, right, there are customers who are still, larger devices still running mainframe for some processing, and they are already using new platforms for IoT, so they have to somehow manage this entire transition, and there will be multi-cloud, cloud hybrid world. So they naturally expect us to be a Switzerland of data across the board, and that's our overall strategy. We will always be there for them. In that context, we work with, we have learned the art of working with their ecosystem. >> John: Yeah. >> So you saw Azure today, and we are very close partners of hundreds of customers. Amazon, hundreds of customers. Google's coming up. So those are common. So we, Adobe, tomorrow you'll have Adobe. >> John: So you're cool with all the cloud players. >> And, you know, I always look at it this way. If you solve for the customer, everybody will work with you, and I think we're doing meaningful work. So that's helping our strategy. But what we have done two very different things with that. We've gone deep in terms of product integration. I mean, you saw today. We are making it easy from a customer experience point of view to get these jobs done, right? If you are spinning up a data warehouse in the cloud, you don't want to repeat the mistakes of the last 20 years. So now it's five clicks, you should be good to go. >> John: Yeah. >> That's an area we've invested a lot to make sure that those experiences are a lot simpler and easier and very native. >> We had Bruce Chizen on earlier. He was implying that you guys have significant R&D, and he was trying to get me to get you the number. I think it was on Twitter. I think I'll ask Neal. I think he's out there already. But it's not so much the numbers. It's about the investment and the mindset you guys have for R&D. I know you had, went with a private equity company. >> Amit: Yes. >> We talked about that. >> You guy are growing. >> So this is a growth company. >> Amit: Yeah. >> You need R&D. >> Absolutely. >> What is the priority? How are you looking at that? How would you talk to the industry and customers about your R&D priorities? >> Well, I think we've been very blessed, and I think our investors, and I think Bruce, when we sit in a board meeting, you know, we always joke around. They have never skimped on investing in products. And I think that we've been, our belief is that we are the innovation leader in our markets. There is a massive opportunity in front of us to obviously capitalize on, and the only way you do it where you innovate, and innovate means we invest. And I tell you we've been very fortunate that the investment in products has continuously increased every year. I mean, this year, forget just the products and technologies. We made, John, double digit million dollar investments in building a brand-new hosting architecture across the world, in Americas, NMEI and APJ, and we benchmarked ourselves against the Amazons and the Azures of the world, not our competitors. So not just products, but taking the cloud infrastructure across the globe, most secure, most... >> So your own infrastructure. >> Absolutely. >> Well, I mean, we run our own stuff. >> Yeah. >> But we leverage both AWS and Azure in that context. But our goal is that we can be in the countries because data should not leave some of those countries. We comply to the biggest regulations. So we've made lots of investment, and hence we can also innovate and get into new product categories. I mean, you see we have a whole new cloud architecture out there. Catalog, security, these are all brand new markets that actually, some of them have all come out since we went private. Actually more innovation has come out of Informatica since we went private than in the three years previous to going private. >> So, you know, let's play a game. Let's say that the catalog, doing very well. Let's say that you, working with Microsoft, working with AWS, you're actually successful at establishing a standard... >> Amit: Yeah. >> ...for how we think about data catalogs in a hybrid, multi-cloud world. Combine that with R&D and products. If you have, in a data-first world, where the next generation of applications are going to be data-first, that catalog gives you an inside edge to an enormous number of new application forms. >> Amit: Yeah. >> How far does Informatica go? >> Well, that's a great question. I mean, I think, I generally believe that in some ways, we are barely scratching the opportunity in front of us. I mean, none of us have seen where this world will go. I mean, who would have imagined, think of all the trends that have happened. Look at the world of social, where it has brought us to bear. I generally think that, look, each company that I talk to, each customer I talk to, and I talk to hundreds of customers across the earth, they all want to become a tech company. They all want to be an Amazon or a Google. And they realize that they will not become an Amazon Google by replicating them. The best way they can become an Amazon Google is to figure out all of the data they have and start using it, right? >> Institutionalizing their work around their data. >> Exactly. So that's where the catalog becomes very handy. It's a great first step to begin that. And in that context, there are Fortune 5000, there's Fortune 10,000, there are mid-market customers. I think we have just literally scratched the surface of that. >> Do you envision catalog-driven applications... >> Amit: Oh, absolutely. >> ...that get into, with the Informatica brand on them? >> Oh, so we actually have, so a great point. We actually made the catalog rest API-driven. So there are customers who are building their applications on the catalog. In fact, I'll give you a preview of that tomorrow. I'll show a demo where Cognizant took our catalog, took CLAIRE within the catalog, used Microsoft's chatbot to create a complete third-party custom application called the Data Concierge, where you can go ask for data. So it's Microsoft chatbot, our CLAIRE engine, and a custom app written by... So the world where I see is that it will be, that is a central nervous system of the platform, and enough custom apps will be written in time. >> It's a real enabler. So I got to ask, and I know we got not a lot of time left, I mean, but I want to get thoughts on cloud native. >> Amit: Yeah. >> 'Cause you have, with containers, you don't have to kill the old to bring in the new. And what you guys are doing is with on-prem and some of the coolness, ease of use around getting the data kind of cataloged in with the metadata, you're enabling potentially developers. Where does this lead us with containers, microservices, service meshes, 'cause that's right around the corner. >> It's happening as we speak. I mean, so we rewrote the cloud platform as I just talked about. It's completely microservices-based, completely. We had to, we had a whole cloud platform. We basically said we're going to rewrite the whole thing. Microservices-based. And it's containerized. So the idea is that A, microservices give you agility, as we all very well know. We can innovate a lot faster. And with the help of containers, you can just rapidly scale, I mean, rapidly deploy. You can test. Dev becomes a whole lot easy. The, I mean, today's cycle is so short. Customers want to do things rapidly. So we are just really helping them be able to do that. >> So you see the data actually being an input into the development process... >> Oh, absolutely. >> ...via microservices and your service mesh. >> I mean, if you don't do that, you don't know what you're building. >> It's going to be a data-first world. My, going back to my point, I think there's an opportunity for you guys to then go to the marketplace with some thought leadership about what does it mean to build data-first applications. Historically we start with a process and we imagine what the data structure's going to look like, we put it in the database, and then there's all the plumbing about interaction and integration. You guys are saying get your data assets, get your data objects rendered inside the catalog and think about the new ways you can put them to work, and you think of your code... >> Amit: Yeah. >> ...as the mechanism by which that happens. >> Flips everything on its ear. >> Amit: Yeah. >> It's a data-first world, and a data-first approach to building applications seems like it's an appropriate next conversation. >> That, I agree with that, and that's a big opportunity, and obviously there's a task at hand to make sure we can help educate everyone to get there. And I think, you know, it'll take some time, but of course that's the, anything which is easy is not interesting. It's a hard problem that where you basically, you solve and you kind of make it a big industry. >> I mean, it's great to see you. We feel like we've been following the journey of the success of you guys. We've been talking, go back four years. >> Amit: Yeah. >> You can go back to thecube.net, look at the tape. You can see the conversations. You guys stayed on task. Great product team, very, you guys are kicking some butt out there. Congratulations. Final question for you. Put you on the spot. Biggest surprise this year for you. What's, obviously the catalog, you mentioned it's been taking off. What surprised you? Anything jump out in terms of successes, speed bumps in the road, architecture trends? What's the big surprise? >> You know, I think I'm actually very warmed up by seeing, I talked about the day zero. You know, it is a data-driven world where we see so many customers looking to come here. We've become the biggest data conference of the industry. In fact, we were reflecting, Informatica World has become the biggest accumulation of people who think data-first. And I think that has been more than any technology. To me, at the end of the day, look, as much technology will come and stay, I'm a big believer it's people that make the difference. >> John: Yeah. >> And I've been seeing all of those people here, seeing them make contributions, learn, and drive change has been my biggest, not only a positive surprise, but biggest, you know, gratification that I've seen at Informatica World. >> And the emphasis of not having such a big hype. I mean, getting excited about new technology is one thing, but the rubber's got to hit the road. You've got to have real performance, real software... >> Yeah. >> ...real results. >> 'Cause the pressure of scale fast, time to market... >> ...all that stuff. >> Right. >> Congratulations, great to see you. Amit Walia, president here at Informatica on products and strategic ecosystems. I'm sure he's going to continue to be busy over the next year when we see him certainly at our next theCUBE event. Amit, great to see you. I'm John Furrier, Peter Burris, live here at Informatica World 2018. It's the largest data-first conference on the planet We'll be right back with more after this short break. (musical sting)

>> Announcer: Live from Las Vegas, it's theCUBE! Covering Informatica World 2018. Brought to you by Informatica. >> Hey, welcome back everyone, it's theCUBE's exclusive coverage here at the Venetian, Live in Las Vegas CUBE coverage. I'm John Furrier the co-host of theCUBE with Peter Burris my co-host for the next two days of wall-to-wall coverage. Our next guest is Andrew Joss, who's the Head of Solutions and Data Governance for Europe, Middle East and Africa, and Latin America for Informatica. Great to have you on, thanks for joining us. >> Thank you! >> I could not stop waiting for this question because your anemia, Europe, Middle East and Europe. GDPR is kicking in this Friday. >> Andrew: Absolutely. >> So we're in May 2018. The big release of the law that kicks into place for GDPR in effect. Two things, what's the mood and then what does it mean? I mean, it's a shot across the bow of the industry. But we know what it means for people but like, what's the impact of this, what's going on? >> So I think we're seeing it at a couple of different levels. I think at a very individual level. I think the awareness of what GDPR potentially means for people, I think we're starting to feel that as individuals, in EMEA. We're seeing increasingly organizations reaching out to us, you know, we want permission to use your information or consensus in coding GDPR. You're customers of ours, here's our new privacy policy. We see lots of this and it's happening from lots of different organizations that we work with. So I think people are starting to see it and feel it, are starting to feel like it's real now, not just something we've been talking about for a long period of time. But I think also in terms of potentially what the impact of this will be, that I think organizations are starting to look at some of the major tenants that sit underneath GDPR, how are they going to address those, and what does that mean for the data subject? Like people like me, for example, I'm a data subject. What does it all mean for me? And I think they realize-- >> John: As an individual you have data rights. >> Exactly. Absolutely. >> The concern I have, I had a big rant on Facebook, it was good conversation, but here's the thing. It's like, you know, when laws came out, like it's so hard being so obviously, when your public, you're ready to go public, you have all the infrastructure to comply with all those regulations, a lot of people aren't prepared for GDPR because, where their, they might not even know where their data is, >> Absolutely. >> what's the format, what's the schema, they don't have mechanisms in place, 'cause there's IT legacy involved. (laughing) I mean GDPR, great on paper, everyone's got their own rights it sounds good, you know, but when you have to get under the hood and saying, hey Enterprise, you know that stuff you've been putting in drives, and the storage administrator quit 10 years ago and, you don't know what's going on and, guess what? You're now liable. >> Andrew: Major issue. >> People were scared. So, this is a problem, how can someone get ready, 'cause just like when people go public they have to be ready hire all these, process stuff, what do you guys see that, I mean Informatica has some solutions, I'm sure, but, what's the client relationship like for you guys as you talk to customers? >> I think it kind of varies, some industries seem to be a little bit more advanced in their thinking so, regulated industries for example, they're kind of used to regulation and compliance, they kind of get a lot of these things, so I think some of those have found this process a little bit easier. I think some industries, this is generally quite new, some of the ideas, some of the practices that come with GDPR I think are also quite new, for some of these industries. >> Internet companies, fast and loose, if you're fast and loose you're going to be doing a lot of work. >> But ultimately, when you think about, a lot of what GDPR brings to the data subject, that people like me and my colleagues, then, a lot of that then is about these rights, and the ability for us to be able to actually take back more control of our data, 'cause fundamentally it is our data. So if we have more control, then it's about how organizations help us with those rights, and help us move along that journey of what we can now do with our data, and what GDPR gives us. >> And just to be clear too, we reported on this in depth with Wikibon, SiliconANGLE, and theCUBE, GDPR it's been clear it's going to be a process. They're going to look for compliance, they're not lookin' for everyone to be like, they want to see directional progress, right, so it's not like the hammer's going to come down tomorrow, but people now, data subjects now can bring claims against companies, so. >> Actually John, I think it's, you're right but, we have a client, the Chief Privacy Officer of one of our clients, made the observation that had the Equifax breach occurred after Friday in Europe, it would have cost that company 160 billion dollars, My guess is what's going to happen is, they're going to look for that direction unless a company has a serious problem, then they're going to use GDPR to levy fines, and generate some, and remunerate back to the people affected, some real relief would you agree with that? >> Actually I see GDPR in a slightly different way, maybe that's just because it feels quite personal to me, because I feel it's something that's going to be a part of my life. And actually I think it's about organizations really respecting my data, and therefore respecting me. So, you know, when we talk about fines, yes I'm sure there's probably going to be some of those. A lot of the customers I talk to are actually, they're worried about reputational damage. You know, what's going to happen to their brand, what's going to happen to their image if something happens? And that, for many organizations, is far more serious and significant than any kind of fine potentially may be, so it's actually-- >> And there's a mega trend goin' on, you're seeing with blockchain and decentralized applications where people who create the value should capture it, hence the personal relationship to your data, and we all look at Facebook and say, hey I signed up for a free app so I could meet my high school friends and see them, do some things on Facebook, but I didn't sign a contract to give you my data to, have the election be thrown in the US, (laughing) so it's kind of like, wait a minute, what're you doin' with my data? >> Talk about blockchain and immutability of the data, if you have, does GDPR make it more difficult to use technologies like blockchain? >> I think organizations just have to look at GDPR and say, you know, it's a principles-based regulation, so it's not going to tell you, you know, the details of how you should do things, but it's tryna take you on a journey around kind of how you can then start to bring a lot more respect to the data subject, because of the data that you're managing and processing for them. The organizations are going to have to look at that and say, how do we take all of this, and how do we start to move it into our environment, whether it be blockchain, or any other technology, how does it apply, and do we have to make some changes, do we have to think tactically or strategically, I think organizations are going to have to look at this and say what does this mean longer term? Because I don't think anyone really knows right now. >> Well I want to get your thoughts on this, as Head of Solutions and Governance we chatted with the Deloitte guys came on earlier, and they kind of laid out, I mean, I'm just paraphrasing the playbook, data engineering, data governance, data enablement, so they're kind of looking at it, you know, as kind of a playbook. Got to do the engineering work to figure out where the data is, throw the catalog in there, MDM, there's a variety of solutions out there, and tools for other things, and then the governance piece is super critical. Then the enablement is where, then you're in an ideal state for a GDPR, or wherever where, everything's foundationally built and engineered and governed, ideally you could have things like consensus, you could have some security, do you see it the same way, and how are you guys at Informatica talking to customers? Does that jive with some of the things that you guys--? >> Yeah, it does, it resonates quite well, so, I think because it's a principles based regulation then, actually that has some potentially quite interesting and beneficial impacts for some of our customers, so a lot of our customers are going through some kind of transformation, mostly digital transformation, and you think about the principles that GDPR gives you, I look at that and I think, but actually some of these are just good data management practices and principles, it happens to be around personal data for GDPR right now but those principles are just valued for probably kind of any kind of data. So if you're on a digital transformation journey, with all the change and with all the opportunity that brings actually these practices and principles for GDPR they should be helping drive things like your digital transformation, and for a lot of our customers change is the only constant they've got. So actually managing all this, whilst everything is changing around you, it's tough for a lot of them. >> Opensource has been a big driver in our industry, we've seen some there, open always beats closed, and having all the open data's key, have you seen any GDPR impact around being open, is there like, opensource groups that are out there helping companies, you guys obviously can get called on, but what dose the customer do, I mean like, Peter and I say hey, maybe we're impacted by GDPR, who do we call? Is there an opensource community that can help with, you know, terms of service, if they want to go down the right roads of data hygiene or data setup cataloging, what do they do, I mean what's the? (laughing) I mean it's the shock, and people going well we're not really kind of where we should be, what do they do? With any movement? >> Yeah, I've seen quite a bit of movement, so, I think probably one of the biggest single challenges that I've seen is, for many organize--many of our customers, they'll be saying to us, okay, so what should we do in this circumstance? And actually that's really tough for us to answer, because it's a principles-based regulation than actually somebody needs to look at that, that's probably the legal or the privacy teams, say well what does that mean for us? How do we take that, and then come up with a set of requirements that says this is what we need to do for our organization, in our markets, in our territory, for example? So there's probably no one-size-fits-all answer, so, there's legal aspects to this, there's privacy aspects, data management, risk, compliance, opensource groups they can give opinion, but it's nothing more than that. >> And they might not have the talent internally to actually understand culturally what the principle is, so they got to call in the consultant, so our integrators, Deloitte-- >> Exactly, exactly. >> But fundamentally, it seems that one of the things GDPR is going to do, is it's going to force companies, force enterprises, to be very explicit and declare what attributes of that personal data they make money with. And be very, and effectively open that up, and be much more, as you said, what'd you call it private subject or? >> Data subjects. >> Data subjects, they're going to have to be more explicit declaring to data subjects, in simple terms, how they're making money off of data, or how they're avoiding that problem. >> Yeah, I think organizations, and I think about some of the privacy notices I've received, recently for example, I think, what organizations are doing, I think they're trying to explain to people, this is the kind of data we have, these are types things that we have to do with it, sometimes it's maybe regulatory, but actually other times it's about, these other types of business activities, so they're starting to be a lot more transparent, I think, in what they're doing with the data. Is it transparent enough? I guess time will tell. And the reaction of data subjects will also be the indicator whether people think that's acceptable or not, I don't think we know yet, it's early days, but actually that change, I think over time what we'll start to see is organizations are going to be looking at the way that they manage data, I think transparency, I think will be a huge topic for a lot of industries, I think that the notion of kind of having a respect for people and their data, and how it then leads to trust. So lots of industries have kind of lost the trust of people around the ability to manage their data, so how do they get that back? Well potentially GDPR might be a way of helping people access to that. >> Many of these guys, they got to get their act together and build up a quality data policy around it. Okay, final question for ya, I know we're tight on time, but I want to get it out there, what do you guys have for solutions for customers, what are you guys offering, specifically for products, that helps them with the compliance, any gap analysis, I mean what do you guys do for customers, what's the solution? >> It's, it's in a couple of different areas, so I'm going to tackle a couple quite specific things, then something slightly a little bit broader, so, organizations, I think you were mentioning earlier, just kind of knowing what their data is. Well actually we have some fantastic technology to go and discover, you know, or to make the discovery of that data, that's great for organizations 'cause that, today, a lot of them are doing it by hand, they're doing it manually, so discovery of data really important, so we have technology in that space. The ability to go and mask an archive, get rid of data, if you don't have a legitimate reason for having data, then why have you got it? So technology to help you, you know, get rid of that data. Other types of technology about being able to connect what you have in terms of your physical data assets, to actually your interpretation of what GDPR means to you and your business, that's fantastic, the ability to connect those together, that's our governance environment, and then technologies around, kind of, building that view of the data subject, so we can then enact all these rights that people like me have now got, but also then too, can sense that we may potentially have to give, how do you associate that with all the complexity of the data? So we have technologies in our massive data management space to do that. But I think probably the one thing that I hear fairly consistently for customers, it's not necessarily about those isolated kind of views, of the technology and how it solves specific problems, I think they're looking at it quite wholistically, and they're looking at solutions that can really automate a lot of this, as much as possible, they're looking for solutions that scale, some of these are very large, complex organizations, it's not small amounts of data, in cases, some cases, it's huge amounts of data, so they're tying to cope with this scale, but they're also looking to solve some very specific problems. So I think there's kind of a combination of things, which I think plays really well, through Informatica's core strengths. >> And it also creates awareness for companies to put data as a strategic centerpiece, not as a side thing, bring it right to the front and center. >> Andrew, thanks for sharing the insight on theCUBE, appreciate your time. theCUBE, live coverage here in Las Vegas at the Venetian, this is exclusive coverage of Informatica World 2018, I'm John Furrier with Peter Burris, stay with us for more, here on day one of two days of coverage. We'll be right back, after this short break. (bubbly music)

>> Announcer: Live, from downtown San Francisco, it's theCUBE covering IBM Chief Data Officer Strategy Summit 2018, brought to you by IBM. >> We're back at the IBM CDO Strategy Summit in San Francisco, we're at the Parc 55, you're watching theCUBE, the leader in live tech coverage. My name is Dave Vellante, and I'm here with Krishna Venkatraman, who is with IBM, he's the Vice President of Data Science and Data Governance. Krishna, thanks for coming on. >> Thank you, thank you for this opportunity. >> Oh, you're very welcome. So, let's start with your role. Your passion is really creating value from data, that's something you told me off-camera. That's a good passion to have these days. So what's your role at IBM? >> So I work for Inderpal, who's GCDO. He's the CDO for the company, and I joined IBM about a year ago, and what I was intrigued by when I talked to him early on was, you know, IBM has so many assets, it's got a huge history and legacy of technology, enormous, copious amounts of data, but most importantly, it also has a lot of experience helping customers solve problems at enterprise scale. And in my career, I started at HP Labs many, many years ago, I've been in a few startups, most recently before I joined IBM, I was at On Deck. What I've always found is that it's very hard to extract information and insights from data unless you have the end-to-end pieces in place, and when I was at On Deck, we built all of it from scratch, and I thought this would be a great opportunity to come to IBM, leverage all that great history and legacy and skill to build something that would allow data to almost be taken for granted. So, in a sense, a company doesn't have to think about the pain of getting value extracted from data, they could just say, you know, I trust data just as I trust the other things in life, like when I go buy a book, I know all the backend stuff is done for me, I can trust the product I get. And I was interested in that, and that's the role that Inderpal offered to me. >> So the opposite of On Deck, really. On Deck was kind of a blank sheet of paper, right? And so now you have a complex organization, as Inderpal was describing this morning, so big challenge. Ginni Rometty at IBM Think talked about incumbent disruptors, so that's essentially what IBM is, right? >> Exactly, exactly. The fact is IBM has a history and a culture of making their customers successful, so they understand business problems really well. They have a huge legacy in innovation around technology, and I think now is the right time to put all of those pieces together, right? To string together a lifecycle for how data can work for you, so when you embark on a data project, it doesn't have to take six months, it could be done in two or three days, because you've cobbled together how to manage data at the backend, you've got the data science and the data science lifecycle worked out, and you know how to deploy it into a business process, because you understand the business process really well. And I think, you know, those are the mismatches that I've seen happen over and over again, data isn't ready for the application of machine learning, the machine learning model really isn't well-suited to the eventual environment in which it's deployed, but I think IBM has all of that expertise, and I feel like it's an opportunity for us to tie that together. >> And everybody's trying to get, I often say, get digital right, you know, your customers, your clients, everyone talks about digital transformation, but it's really all about the data, isn't it? Getting the data right. >> Getting the data right, that's where it starts. Tomorrow, I'm doing a panel on trust, you know, we can talk about the CDO and all the great things that are happening and extracting value, but unless you have trust at the beginning and you're doing good data governance, and you're able to understand your data, all of the rest will never happen. >> But you have to have both, alright? Because if you have trust without the data value, then okay. And you do see a lot of organizations just focusing, maybe over-rotating on that privacy and trust and security, for good reason, how do you balance that information as an asset versus liability equation? Because you're trying to get value out of it, and at the same time, you're trying to protect your organization. >> Yeah. I think it's a virtuous cycle, I think they build on each other. If customers trust you with their data, they're going to give you more of it, because they know you're going to use it responsibly, and I think that's a very positive thing, so I actually look at privacy and trust as enablers to create value, rather than somehow they're in competition. >> Not a zero-sum game. >> Not at all. >> Let's talk some more about that, I mean, when you think about it, because I've heard this before, GDPR comes up. Hey, we can turn GDPR into an opportunity, it's not just this onerous, even though it is, regulatory imposition, so maybe some examples or maybe talk through how organizations can take the privacy and trust part of the equation and turn it into value. >> So very simply, what does GDPR promise, right? It's restoring the fundamental rights of data subjects, in terms of their ownership of their data and the processing of their data and the ability to know how that data is used at any point in time. Now imagine if you're a data scientist and you could, for a problem that you're trying to solve, have the same kind of guarantees. You know all about the data, you know where it resides, you know exactly what it contains. They're very similar, you know? They both are asking for the same type of information. So, in a sense, if you solve the GDPR problem well, you have to really understand your data assets very well, and you have to have it governed really well, which is exactly the same need for data scientists. So, in a way, I seem them as, you know, they're twins, separated at some point, but... >> What's interesting, too, is you think about, we were sort of talking about this off-camera, but now, you're one step away from going to a user or customer and saying here, here's your data, do what you like with it. Now okay, in the one case, GDPR, you control it, sort of. But the other is if you want to monetize your own data, why pay the search company for clicking on an ad? Why not monetize your own data based on your reputation or do you see a day where consumers will actually be able to own, truly own their own data? >> I think, as a consumer, as well as a data professional, I think that the technologies are falling into place for that model to possibly become real. So if you have something that's very valuable that other people want, there should be a way for you to get some remuneration for that, right? And maybe it's something like a blockchain. You contribute your data and then when that data is used, you get some little piece of it as your reward for that. I don't know, I think it's possible, I haven't really... >> Nirvana. I wonder if we can talk about disruption, nobody talks about that, we haven't had a ton of conversations here about disruption, it seems to be more applying disciplines to create data value, but coming from the financial services industry, there's an industry that really hasn't been highly disrupted, you know, On Deck, in a way, was trying to disrupt. Healthcare is another one that hasn't been disrupted. Aerospace really hasn't been disrupted. Other industries like publishing, music, taxis, hotels have been disrupted. The premise is, it's the data that enables that disruption. Thoughts on disruption from the standpoint of your clients and how you're helping them become incumbent disruptors? >> I think sometimes disruption happens and then you look back and you say, that was disrupted after all, and you don't notice it when it happens, so even if I look at financial services and I look at small business lending, the expectations of businesses have changed on how they would access capital in that case. Even though the early providers of that service may not be the ones who win in the end, that's a different matter, so I think the idea that, you know, and I feel like this confluence of technologies, where's there's blockchain or quantum computing or even regulation that's coming in, that's sort of forcing certain types of activities around cleaning up data, they're all happening simultaneously. I think we will see certain industries and certain processes transform dramatically. >> Orange Bank was an example that came up this morning, an all-digital bank, you can't call them, right? You can't walk into their branch. You think banks will lose control of the payment systems? They've always done a pretty good job of hanging onto them, but... >> I don't know. I think, ultimately, customers are going to go to institutions they trust, so it's all going to end up with, do you trust the entity you've given your precious commodities to, right? Your data, your information, I think companies that really take that seriously and not take it as a burden are the ones who are going to find that customers are going to reach out to them. So it's more about not necessarily whether banks are going to lose control or whether... Which banks are going to win, is the way I would look at it. >> Maybe the existing banks might get trouble, but there's so many different interesting disruption scenarios, I mean, you think about Watson in healthcare, maybe we're at the point already where machines can make better diagnoses than doctors. You think about retail, and certain retail won't go away, obviously grocery and maybe high-end luxury malls won't go away, but you wonder about the future of retail as a result of this data disruption. Your thoughts? >> On retail? I do feel like, because the data is getting more, people are going to have more access to their own information, it will lead to a change in business models in certain cases. And the friction or the forces that used to keep customers with certain businesses may dissolve, so if you don't have friction, then it's going to end up with value and loyalty and service, and those are the ones I think that will thrive. >> Client comes to you, says, Krishna, I'm really struggling with my overall data strategy, my data platform, governance, skills, all the things that Inderpal talked about this morning, where do I start? >> I would start with making sure that the client has really thought about the questions they need answered. What is it that you really want to answer with data, or it doesn't even have to be with data, for the business, with its strategy, with its tactics, there have to be a set of questions framed up that are truly important to that business. And then starting from there, you can say, you know, let's slow it down and see what technologies, what types of data will help support answering those questions. So there has to be an overarching value proposition that you're trying to solve for. And I see, you know, that's why when, the way we work in our organization is, we look at use cases as a way to drive the technology adoption. What are the big business processes you are trying to transform, what's the value you expect to create, so we have a very robust discovery process where we ask people to answer those types of questions, we help them with it. We ask them to think through what they would do if they had the perfect answer, how they will implement it, how they will measure it. And then we start working on the technology. I often think technology is an easier question to answer once you know what you want to ask. >> Totally. Is that how you spend your time, mostly working with the lines of business, trying to help them sort of answer those questions? >> That is one part of my charter. So my charter involves basically four areas, the first is data governance, just making sure that we are creating all the tools and processes so that we can guarantee that when data is used, it is trusted, it is certified, and that it's always going to be reliable. The second piece is building up a real data competency and data science competency in the organization, so we know how to use data for different types of business value, and then the third is actually taking these client engagements internally and making sure that they are successful. So our model is what we call co-creation. We ask business teams to contribute their own resources. Data engineers, data scientists, business experts. We contribute specialized skills as well. And so we're jointly in the game together, right? So that's the third piece. And the last piece is, we're building out this platform that Inderpal showed this morning, that platform needs product management, so we are also working on, what are the fundamental pieces of functionality we want in the platform, and how do we make sure they're on the roadmap and they're prioritized in the right way. >> Excellent. Well, Krishna, thanks very much for coming to theCUBE, it was a pleasure meeting you. >> Thanks. >> Alright, keep it right there everybody, we'll be back with our next guest. You're watching theCUBE live from IBM CDO Summit in San Francisco. We'll be right back. (funky electronic music) (phone dialing)

(dramatic music) >> Narrator: Live from Fisherman's Wharf in San Francisco. It's theCube. Covering IBM Chief Data Officer Strategy Summit Spring 2017. Brought to you by IBM. >> Hey, welcome back, everybody. Jeff Frank here with theCube. We are wrapping up day one at the IBM CEO Strategy Summit Spring 2017 here at the Fisherman's Wharf Hyatt. A new venue for us, never been here. It's kind of a cool venue. Joined by Peter Burris, Chief Research Officer from Wikibon, and we're excited to have practitioners. We love getting practitioners on. So we're joined by this segment by Bruce Tyler. He's a VP Data Analytics for IBM Global Business Services. Bruce, nice to see you. >> Thank you. >> And he's brought along Fawad Butt, the Chief Data Governance Officer for Kaiser Permanente. Welcome. >> Thank you, thank you. >> So Kaiser Permanente. Regulated industry, health care, a lot of complex medical issues, medical devices, electronic health records, insurance. You are in a data cornucopia, I guess. >> It's data heaven all the way. So as you mentioned, Kaiser is a vertically integrated organization, Kaiser Permanente is. And as such the opportunity for us is the fact that we have access to a tremendous amount of data. So we sell insurance, we run hospitals, medical practices, pharmacies, research labs, you name it. So it's an end to end healthcare system that generates a tremendous amount of dataset. And for us the real opportunity is to be able to figure out all the data we have and the best uses for it. >> I guess I never really thought of it from the vertical stack perspective. I used to think it was just the hospital, but the fact that you have all those layers of the cake, if you will, and can operate within them, trade data within them, and it gives you a lot of kind of classic vertical stack integration. That fits. >> Very much so. And I didn't give you the whole stack. I mean, we're actually building a medical school in Southern California. We have a residency program in addition to everything else we've talked about. But yeah, the vertical stack does provide us access to data and assets related to data that are quite unique. On the one side, it's a great opportunity. On the other side, it has to be all managed and protected and served in the best interest of our patrons and members. >> Jeff: Right, right. And just the whole electronic health records by themselves that people want access to that, they want to take them with. But then there's all kinds of scary regulations around access to that data. >> So the portability, I think what you're talking about is the medical record portability, which is becoming a really new construct in the industry because people want to be able to move from practitioner to practitioner and have that access to records. There are some regulation that provide cover at a national scale but a lot of this also is impacted by the states that you're operating in. So there's a lot of opportunities where I can tell some of the regulation in this space over time and I think that will, then we'll see a lot more adoption in terms of these portability standards which tend to be a little one off right now. >> Right, right. So I guess the obvious question is how the heck do you prioritize? (laughter) You got a lot of things going on. >> You know, I think it's really the standard blocking/tackling sort of situation, right? So one of the things that we've done is taken a look at our holistic dataset end to end and broken it down into pieces. How do you solve this big problem? You solve it by piecing it out a little bit. So what we've done is that we've put our critical dataset into a set of what we call data domains. Patient, member, providers, workers, HR, finance, you name it. And then that gives us the opportunity to not only just say how good is our data holistically but we can also go and say how good is our patient data versus member data versus provider data versus HR data. And then not only just know how good it is but it also gives us the opportunity to sort of say, "Hey, there's no conceivable way we can invest "in all 20 of these areas at any given point." So what's the priority that aligns with business objectives and goals? If you think about corporate strategy in general, it's based on customers and demand and availability and opportunities but now we're adding one more tool set and giving that to our executives. As they're making decisions on investments in longer term, and this isn't just KP, it's happening across industries, is that the data folks are bringing another lens to the table, which is to say what dataset do we want to invest in over the course of the next five years? If you had to choose between 20, what are the three that you prioritize first versus the other. So I think it's another lever, it's another mechanism to prioritize your strategy and your investments associated with that. >> But you're specifically focused on governance. >> Fawad: I am. >> In the health care industry, software for example is governed by a different set of rules as softwares in other areas. Data is governed by a different set of rules than data is governed in most other industries. >> Fawad: Correct. >> Finance has its own set of things and then some others. What does data governance mean at KP? Which is a great company by the way. A Bay Area company. >> Absolutely. >> What does it mean to KP? >> It's a great question, first of all. Every data governance program has to be independent and unique because it should be trying to solve for a set of things that are relevant in that context. For us at KP, there are a few drivers. So first is, as you mentioned, regulation. There's increased regulation. There's increased regulatory scrutiny in pressure. Some things that have happened in financial services over the last eight or ten years are starting to come and trickle in to the healthcare space. So there's that. There's also a changing environment in terms of how, at least from an insurance standpoint, how people acquire health insurance. It used to be that your employer provided a lot of that, those services and those insurances. Now you have private marketplaces where a lot of people are buying their own insurance. And you're going from a B2B construct to a B2C construct in certain ways. And these folks are walking around with their Android phones or their iPhones and they're used to accessing all sorts of information. So that's the customer experience that you to to deliver to them. So there's this digital transformation that's happening that's driving some of the need around governance. The other areas that I think are front and center for us are obviously privacy and security. So we're custodians of a lot of datasets that relate to patients' health information and their personal information. And that's a great responsibility and I think from a governance standpoint that's one of the key drivers that define our focus areas in the governance space. There are other things that are happening. There's obviously our mission within the organization which is to deliver the highest coverage and care at the lowest cost. So there's the ability for us to leverage our data and govern our data in a way which supports those two mission statements, but the bigger challenge in nuts and bolts terms for organizations like ours, which are vertically integrated, is around understanding and taking stock of the entire dataset first. Two, protecting it and making sure that all the defenses are in place. But then three, figuring out the right purposes to use this, to use the data. So data production is great but data consumption is where a lot of the value gets captured. So for us some of the things that data governance facilitates above all is what data gets shared for what purposes and how. Those are things that an organization of our size deliver a tremendous amount of value both on the offensive and the defensive side. >> So in our research we've discovered that there are a lot of big data functions or analytic functions that fail because they started with the idea of setting up the infrastructure, creating a place to put the data. Then they never actually got to the use case or when they did get to the use case they didn't know what to do next. And what a surprise. No returns, lot of costs, boom. >> Yep. >> The companies that tend to start with the use case independently individual technologies actually have a clear path and then the challenge is to accrete knowledge, >> Yes. >> accrete experience and turn it into knowledge. So from a governance standpoint, what role do you play at KP to make sure that people stay focused in use cases, that the lessons you learn about pursuing those use cases then turn to a general business capability in KP. >> I mean, again, I think you hit it right on the head. Data governance, data quality, data management, they're all great words, right? But what do they support in terms of the outcomes? So from our standpoint, we have a tremendous amount of use cases that if we weren't careful, we would sort of be scatterbrained around. You can't solve for everything all at once. So you have to find the first set of key use cases that you were trying to solve for. For us, privacy and security is a big part of that. To be able to, there's a regulatory pressure there so in some cases if you lose a patient record, it may end up costing you $250,000 for a record. So I think it's clear and critical for us to be able to continue to support that function in an outstanding way. The second thing is agility. So for us one of the things that we're trying to do with governance and data management in general, is to increase our agility. If you think about it, a lot of companies go on these transformation journeys. Whether it's transforming HR or trying to transform their finance functions or their business in general, and that requires transforming their systems. A lot of that work, people don't realize, is supported and around data. It's about integrating your old data with the new business processes that you're putting out. And if you don't have that governance or that data management function in place to be able to support that from the beginning or have some maturity in place, a lot of those activities end up costing you a lot more, taking a lot longer, having a lower success rate. So for us delivering value by creating additional agility for a set of activities that as an organization, we have committed to, is one for of core use cases. So we're doing a transformation. We're doing some transformation around HR. That's an area where we're making a lot of investments from a data governance standpoint to be able to support that as well as inpatient care and membership management. >> Great, great lessons. Really good feedback for fellow practitioners. Bruce, I want to get your perspective. You're kind of sitting on the other side of the table. As you look at the experience at Kaiser Permanente, how does this equate with what you're seeing with some of your other customers, is this leading edge or? >> Clearly on point. In fact, we were talking about this before we came up and I'm not saying that you guys led, we led the witness here but really how do you master around the foundational aspects around the data, because at the end of the day it's always about the data. But then how do you start to drive the value out of that and go down that cognitive journey that's going to either increase value onto your insights or improve your business optimization? We've done a healthy business within IBM helping customers go through those transformation processes. I would say five years ago or even three years ago we would start big. Let's solve the data aspect of it. Let's build the foundational management processes around there so that it ensures that level of integrity and trusted data source that you need across an organization like KP because they're massive because of all the different types of business entities that they have. So those transformation initiatives, they delivered but it was more from an IT perspective so the business partners that really need to adopt and are going to get the value out of that were kind of in a waiting game until that came about. So what we're seeing now is looking at things around from a use case-driven approach. Let's start small. So whether you're looking at trying to do something within your call center and looking at how to improve automation and insights in that spec, build a proof of value point around a subset of the data, prove that value, and those things can typically go from 10 to 12 weeks, and once you've demonstrated that, now how do can you scale? But you're doing it under your core foundational aspects around the architecture, how you're going to be able to sustain and maintain and govern the data that you have out there. >> It's a really important lesson all three of you have mentioned now. That old method of let's just get all the infrastructure in place is really not a path to success. You getting hung up, spend a lot of money, people get pissed off and oh by the way, today your competitors are transforming right around you while you're >> Unless they're also putting >> tying your shoes. >> infrastructure. >> Unless they're also >> That's right. (laughter) >> tying their shoes too. >> Build it and they will come sounds great, but in the data space, it's a change management function. One of my favorite lines that I use these days is data management is a team sport. So this isn't about IT, or this isn't just about business, and can you can't call business one monolith. So it's about the various stakeholders and their needs and your ability to satisfy them to the changes you're about to implement. And I think that gets lost a lot of times. It turns into a technical conversation around just capability development versus actually solving and solutioning for that business problem set that are at hand. >> Jeff: Yeah. >> Peter: But you got to do both, right? >> You have to. >> Bruce: Absolutely, yeah. >> Can I ask you, do we have time for another couple of questions? >> Absolutely. >> So really quickly, Fawad, do you have staff? >> Fawad: I do. >> Tell us about the people on your staff, where they came from, what you're looking for. >> So one of the core components of data governance program are stewards, data stewards. So to me, there are multiple dimensions to what stewards, what skills they should have. So for stewards, I'm looking for somebody that has some sort of data background. They would come from design, they would come from architecture, they would come from development. It doesn't really matter as long as they have some understanding. >> As long as you know what a data structure is and how you do data monitoring. >> Absolutely. The second aspect is that they have to have an understanding of what influence means. Be able to influence outcomes, to be able to influence conversations and discussions way above their pay grade, so to be able to punch above your weight so to speak in the influence game. And that's a science. That's a very, very definitive science. >> Yeah, we've heard many times today that politics is an absolute crucial game you have to play. >> It is part of the game and if you're not accounting for it, it's going to hit you in the face when you least expect it. >> Right. >> And the third thing is, I look for people that have some sort of an execution background. So ability to execute. It's great to be able to know data and understand data and go out and influence people and get them to agree with you, but then you have to deliver. So you have to be able to deliver against that. So those are the dimensions I look at typically when I'm looking at talent as it relates particularly to stewardship talent. In terms of where I find it, I try to find it within the organization because if I do find it within the organization, it gives me that organizational understanding and those relationship portfolios that people bring to the table which tend to be part of that influence-building process. I can teach people data, I can teach them some execution, I can't teach them how to do influence management. That just has to-- >> You can't teach them to social network. >> Fawad: (laughing) That's exactly right. >> Are they like are the frustrated individuals that have been seen the data that they're like (screams) this is-- >> They come from a lot of different backgrounds. So I have a steward that is an attorney, is a lawyer. She comes from that background. I have a steward that used to be a data modeler. I have a steward that used to run compliance function within HR. I have a steward that comes from a strong IT background. So it's not one formula. It's a combination of skills and everybody's going to have a different set of strengths and weaknesses and as long as you can balance those out. >> So people who had an operational role, but now are more in an execution setup role. >> Fawad: Yeah, very much so. >> They probably have a common theme, though, across them that they understand the data, they understand the value of it, and they're able to build consensus to make an action. >> Fawad: That's correct. >> That's great. That's perfect close. They understand it and they can influence, and they can get to action. Pretty much sums it up, I think so. All right. >> Bruce: All right thank you. >> Well, thanks a lot, Bruce and Fawad for stopping by. Great story. Love all the commercials on the Warriors, I'm a big fan and watch KNBR. (laughter) But really a cool story and thanks for sharing it and continued success. >> Thank you for the opportunity. >> Absolutely. All right, with Peter Burris, I'm Jeff Frank. You're watching theCube from the IBM Chief Data Officer Strategy Summit Spring 2017 from Fisherman's Wharf, San Francisco. We'll be right back after this short break. Thanks for watching. (electronic music)

>> Narrator: Live from Dublin, Ireland. It's theCUBE, covering Hadoop Summit Europe 2016. Brought to you by Hortonworks. Now your host, John Furrier and Dave Vellante. >> Okay, welcome back here, we are here live in Dublin, Ireland, it's theCUBE SiliconANGLEs flagship program where we go out to the events and extract the signal from the noise, I'm John Furrier, my cohost Dave Vellante, our next guest is Emer Coleman who's with Disruption Limited, Open Data Governance Board in Ireland and Transport API, a growing startup built self-sustainable, growing business, open data, love that keynote here at Hadoop Summit, very compelling discussion around digital goods, digital future. Emer, welcome to theCUBE. >> It's great to be here. >> So what was your keynote? Let's just quickly talk about what you talked about, and then we can get in some awesome conversation. >> Sure. So the topic yesterday was we need to talk about techno ethics. So basically, over the last couple of months, I've been doing quite a lot of research on ethics and technology, and many people have different interpretations of that, but yesterday I said it's basically about three things. It's about people, it's about privacy, and it's about profits. So it's asking questions about how do we look at holistic technology development that moves away from a pure technocratic play and looks at the deep societal impacts that technology has. >> One of the things that we're super excited about and passionate about is this new era of openness going to a whole another level. Obviously, open source tier one software development environment, cloud computing allows for instant access to resources, almost limitless at this point, as you can project it forward with Moore's Law and whatnot. But the notion that digital assets are not just content, it's data, it's people, it's the things you mentioned about, create a whole new operating environment or user experience, user expectations with mobile phones and Internet of Things and Transport API which you have, if it moves, you capture it, and you're providing value there. So a whole new economy is developing around digital capital. Share your thoughts around this, because this is an area that you're passionate about, you've just done work here, what's your thoughts on this new digital economy, digital capital, digital asset opportunity? >> I think there's huge excitement about the digital economy, isn't there? And I think one of the things I'm concerned about is that that excitement will lead us to the same place that we are now, where we're not really thinking through what are the equitable distribution in that economy, because it seems to me that the spoils are going to a very tiny elite at the tops. So if you look at Instagram, 13 employees when it was purchased by Facebook for a billion dollars, but that's all our stuff, so I'm not getting any shares in the billion, those 13 people are. That's fantastic that you can build a business, build it to that stage and sell, but you have to think about two things, really: what are we looking at in terms of sustainable businesses into the future that create ethical products, and also the demands from citizens to get some value for their data back, because we're becoming shadow employees, we're shadow employees of Google, so when we email, we're not just corresponding, we're creating value for that company. >> And Facebook is a great example. >> And Facebook, and the thing is, when we were at the beginning of that digital journey, it was quite naive. So we were very seduced by free, and we thought, "This is great," and so we're happy with the service. And then the next stage of that, we realize what if we're not paying for the service, we're the product? >> John: Yeah. >> But we were too embedded in the platform to extricate ourselves. But now, I think, when we look at the future of work and great uncertainty that people are facing, when their labor's not going to be required to the same degree, are we going to slavishly keep producing capital and value for companies like Google, and ask for nothing more than the service in return? I don't think so. >> And certainly, the future will be impacted, and one of the things we see now in our business of online media and online open data, is that the data's very valuable. We see that, I'll say data is the new capital, new oil, whatever phrases of the day is used, and the brand marketers are the first ones to react to it, 'cause they're very data driven. Who are you, how do I sell stuff to you? And so what we're seeing is, brand marketers are saying, "Hey, I'm going to money to try to reach out to people, "and I'm going to activate that base and connect with, "engage with them on Facebook or other platform. "I'm going to add value to your Facebook or Google platform, "but yet I'm parasitic to your platform for the data. "Why just don't I get it directly?" So again, you're starting to see that thinking where I don't want to be a parasite or parasitic to a network that the value's coming from. The users have not yet gotten there, and you're teasing that out. What's your thoughts there, progression, where we're at, have people realized this? Have you seen any movement in the industry around this topic? >> No, I think there's a silence around... Technology companies want to get all the data they can. They're not going to really declare as much as they should, because it bends their service model a bit. Also, the data is emergent. Zuckerberg didn't start Facebook as something that was going to be a utility for a billion people, he started it as a social network for a university. And what grew out of that, we learned as we went along. So I'm thinking, now that we have that experience, we know that happens, so let's start the thinking now. And also, this notion of just taking data because you can, almost speculatively getting data at the point of source, without even knowing what you want it for but thinking, "I'm going to monetize this in the end." Jaron Lanier in his book Who Owns The Future talks about micro licensing back content. And I think that's what we need to do. We start, at the very beginning, we need to start baking in two things: privacy by design and different business models where it's not a winner takes all. It's a dialog between the user and the service, and that's iterated together. >> This idea that it's not a zero sum game is very important, and I want to go back to your Instagram and Facebook example. At its peak, I think Eastman Kodak had hundreds of thousands of employees, maybe four or five hundred, 450,000 employees, huge. Facebook has many many more photos, but maybe a few thousand employees? Wow, so all the jobs are gone, but at the same time, we don't want to be protecting the past from the future, so how do you square that circle? >> Correct, but I think what we know is that the rise of robotics and software is going to eat jobs, and basically, there's going to be a hollowing out of the middle class. You know, for sure, whether it's medicine, journalism, retail, exactly. >> Dave: It's not future, it's now. (laughs) >> Exactly. So we maybe come into a point where large swaths of people don't have work. Now, what do you do in a world where your labor is no longer required? Think about the public policy implications of that. Do we say you either fit in this economy or you die? Are we going to look at ideas which they are looking at in Europe, which is like a universal wage? And all of these things are a challenge to government, because they're going to have a citizenry who are not included in this brave new world. So some public policy thinking has to go into what happens when our kids can't get jobs. When the jobs that used to be done by people like us are done by machines. I'm not against the movement of technology, what I'm saying is there are deep societal implications that need some thinking, because if we get to a point where we suddenly realize, if all of these people who are unemployed and can't get work, this isn't a future we envisioned where robots would take all the crap jobs and we would go off to do wonderful things, like how are we going to bring the bacon home? >> It seems like in a digital world that the gap is creativity to combine technologies and knowledge. I find that it's scary when you talk about maybe micromanaging wages and things like that, education is the answer, but that's... How do you just transfer that knowledge? That's sort of the discussion that we're having in the United States anyway. >> I think some of the issue is that the technology is so, we're kind of seduced by simplicity. So we don't see the complexity underneath, and that's the ultimate aim of a technology, is to make something so simple, that complexity is masked. That's what the iPhone did wonderfully. But that's actually how society is looking now. So we're seduced by this simplicity, we're not seeing the complexity underneath, and that complexity would be about what do we do in a world where our labor is no longer required? >> And one of the things that's interesting about the hollowing of the middle class is the assumption is there's no replacements, so one of the things that could be counter argued is that, okay, as the digital natives, my daughter, she's a freshman in high school, my youngest son's eighth grade, they're natives now, so they're going to commit. So what is the replacement capital and value for companies that can be sustained in the new economy versus the decay and the darwinism of the old? So the digital darwinism aspect's interesting, that's one dilemma. The other one is business models, and I want to get your thoughts on this 'cause this is something we were teasing out with this whole value extraction and company platform issue. A company like Twitter. Highly valuable company, it's a global network of people tweeting and sharing, but yet is under constant pressure from Wall Street and investors that they basically suck. And they don't, they're good, people love Twitter, so they're being forced to behave differently against their mission because their profit motive doesn't really match maybe something like Facebook, so therefore they're instantly devalued, yet the future of someone connecting on Twitter is significantly high. That being said, I want to get your thoughts on that and your advice to Twitter management, given the fact it is a global network. What should they do? >> It's the same old capitalism, just it's digital, it's a digital company, it's a digital asset. It's the same approach, right? Twitter has been a wonderful thing. I've been a Twitter user for years. How amazing, it's played a role in the Arab Spring, all sorts of things. So they're really good, but I think you need as a company, so for example, in our company, in Transport API, we're not really looking to build to this massive IPO, we're trying to build a sustainable company in a traditional way using digital. So I think if you let yourself be seduced by the idea of phenomenal IPO, you kind of take your eye off the ball. >> Or in case this, in case you got IPOed, now you're under pressure to produce-- >> Emer: Absolutely, yeah. >> Which changes your behavior. But in Twitter's management defense, they see the value of their product. Now, they got there by accident and everyone loves it, but now they're not taking the bait to try to craft a short term solution to essentially what is already a valuable product, but not on the books. >> Yes, and also I think where the danger is, we know that their generation shifts across channel. So teenagers probably look at Facebook, I think one of them said, like an awkward family dinner they can't quite leave. But for next gen, they're just not going to go there, 'cause that's where your grandmother is. So the same is true of Twitter and Snapchat, these platforms come and go. It's an interesting phenomenon then to see Wall Street putting that much money into something which is essentially quite ephemeral. I'm not saying that Twitter won't be around for years, it may be, but that's the thing about digital, isn't it? Something else comes in and it's well, that becomes the platform of choice. >> Well, it's interesting, right? Everybody, us included, we criticize the... Michael Dell calls it the 90 day shock clock. But it's actually worked out pretty well, I mean, economically, for the United States companies. Maybe it doesn't in the future. What are your thoughts on that, particularly from a European perspective? Where you're reporting maybe twice a year, there's not as much pressure, but yet from a technology industry standpoint, companies outside the Silicon Valley in particular seem to be less competitive, why? >> For example, in our company, in Transport API, we've got some pretty heavyweight clients, we have a wonderful angel investor who has given us two rounds of investment. And it isn't that kind of avaricious absolutely built this super price. And that's allowed us to build from starting off with 2, now to a team of 10, and we're just about coming into break even, so it's doable. But I think it's a philosophy. We didn't want necessarily to build something huge, although we want to go global, but it was let's do this in a sustainable way with reasonable wages, and we've all put our own soul and money into it, but it's a different cultural proposition, I think. >> Well, the valuations always drive the markets. It's interesting too, to your point about things come and go channels, kind of reminds me, Dave and I used to joke about social networks like nightclubs, they're hot and then it's just too crowded and nobody goes there, as Yogi Bear would say. And then they shift and they go out of business, some don't open with fanfare, no one goes 'cause it's got different context. You have a contextual challenge in the world now. Technology can change things, so I want to ask you about identity 'cause there was a great article posted by the founder of the company called Secret which is one of these anonymous apps like Yik Yak and whatnot, and he shut it down. And he wrote a post, kind of a postmortem, saying, "These things come and go, they don't work, "they're not sustainable because there's no identity." So the role of identity in a social global virtual world, virtual being not just virtual reality, is interesting. You live in a world, and your company, Transport API, provides data which enables stuff and the role of identity. So anonymous versus identity, thoughts there, and that impact to the future of work? If you know who you're dealing with, and if they're present, these are concepts that are now important, presence, identity, attention. >> And that's the interesting thing, isn't it? Who controls that identity? Mark Zuckerberg said, "You only have one identity," which is what he said when he set up Facebook. You think, really? No, that's what a young person thinks. When we're older, we know. >> He also said that young people are smarter than older people. >> Yeah, right, okay. (John laughs) He could be right there, he could be right there, but we all have different identities in different parts of our lives. Who we are here, the Hadoop summit is different from what we're at home to when we're with friends. So identity is a multifaceted thing. But also, who gets to determine your identity? So I have 16 years of my search life and Google. Now, who am I in that server, compared to who I am? I am the sum total of my searches. But I'm not just the sum total of my searches, am I? Or even that contextualized, so I'll give you an example. A number of years ago I was searching for a large, very large waterproof plastic bag. And I typed it in, and I thought, "Oh my god, that sounds like I'm going to murder my husband "and try to bury him." (John and Dave laugh) It was actually-- >> John: Into the compost. >> Right, right. And I thought, "Oh my god, what does this look like "on the other side?" Now, it was actually for my summer garden furniture. But the point is, if you looked at that in an analytic way, who would I be? And so I think identity is very, you know-- >> John: Mistaken. >> Yeah, and also this idea of what Frank Pasquale calls the black box society. These secret algorithms that are controlling flows of money and information. How do they decide what my identity is? What are the moral decisions that they make around that? What does it say if I search for one thing over another? If I search constantly for expensive shoes, does that make me shallow? What do these things say? If I search for certain things around health. >> And there's a value judgment now associated with that that you're talking about, that you do not control. >> Absolutely, and which is probably linked to other things which will determine things like whether I get credit or not, but these can almost be arbitrary decisions, 'cause I have no oversight of the logic that's creating that decision making algorithm. So I think it's not just about identity, it's about who's deciding what that identity is. >> And it's also the reality that you're in, context, situations. Dark side, bright side of technology in this future where this new digital asset economy, digital capital. There's going to be good and bad, education can be consumed non-linear, new forms of consumptions, metadata, as you're pointing out, with the algorithms. Where do you see some bright spots and where do you see the danger areas? >> I think the great thing is, when you were saying software is the future. It's our present, but it's going to be even more so in our future. Some of the brightest brains in the world are involved in the creation of new technology. I just think they need to be focusing a bit more of that intellectual rigor towards the impact they're having on society and how they could do it better. 'Cause I think it's too much of a technocratic solution. Technologists say, "We can do this." The questions is, should they? So I think what we need to do is to loop them back into the more social and philosophical side of the discussion. And of course it's a wonderful thing, hopefully technology is going to do amazing things around health. We can't even predict how amazing it's going to be. But all I'm saying is that, if we don't ask the hard questions now about the downsides, we're going to be in a difficult societal position. But I'm hoping that we will, and I'm hoping that raising issues like techno ethics will get more of that discussion going. >> Well, transparency and open data make a big difference. >> Emer: Absolutely. >> Well, and public policy, as you said earlier, can play a huge role here. I wonder if you could give us your perspective on... Public policy, we're in the US most of the time, but it's interesting when we talk to customers here. To hear about the emphasis, obviously, on privacy, data location and so forth, so in the digital world, do you see Europe's emphasis and, I think, leading on those types of topics as an advantage in a digital world, or does it create friction from an economic standpoint? >> Yeah, but it's not all about economics. Friction is a good thing. There are some times when friction is a good thing. Most technologists think all friction is bad. >> Sure, and I'm not implying that it's necessarily good or bad, I'm curious though, is it potentially an economic advantage to have thought through and have policy on some of those issues? >> Well, what we're seeing here-- >> Because I feel like the US is a ticking time bomb on a lot of these issues. >> I was talking to VCs, some VC friends of mine here in the UK, and what they said they're seeing more and more, VCs asking what we call SMEs, small to medium enterprises, about their data policies, and SMEs not being able to answer those questions, and VCs getting nervous. So I think over time it's going to be a competitive advantage that we've done that homework, that we're basically not just rushing to get more users, but that we're looking at it across the piece. Because, fundamentally, that's more sustainable in the longer term. People will not be dumb too forever. They will not, and so doing that thinking now, where we work with people as we create our technology products, I think it's more sustainable in the long term. When you look at economics, sustainability is really important. >> I want to ask you about the Transport API business, 'cause in the US, same thing, we've seen some great openness of data and amazing innovations that have come out of nowhere. In some cases, unheard of entrepreneurs and/or organizations that better society for the betterment of people, from delivering healthcare to poor areas and whatnot. What has been the coolest thing, or of things you've seen come out of your enablement of the transport data. Use cases, have you seen any things that surprised you? >> It's quite interesting, because when I worked for the mayor of London as his director of digital projects, my job was to set up the London data store, which was to open all of London's public sector data. So I was kind of there from the beginning as a lobbyist, and when I was asking agencies to open up their data, they'd go, "What's the ROI?" And I'd just say, "I don't know." Because government's one and oh, I'm saying that was a chicken and egg, you got to put it out there. And we had a funny incident where some of the IT staff in transport for London accidentally let out this link, which is to the tracker net feed, and that powers the tube notice boards that says, "Your next tube is in a minute," whatever. And so the developer community went, "Ooh, this is interesting." >> John: Candy! >> Yeah, and of course, we had no documentation with it because it kind of went out under the radar. And one developer called Mathew Somerville made this map which showed the tubes on a map in real time. And it was like surfacing the underground. And people just thought, "Oh my god, that is amazing." >> John: It's illuminating. >> Yeah. It didn't do anything, but it showed the possibility. The newspapers picked it up, it was absolutely brilliant example, and the guy made it in half a day. And that was the first time people saw their transport system kind of differently. So that was amazing, and then we've seen hundreds of different applications that are being built all the time. And what we're also seeing is integration of transport data with other things, so one of our clients in Transport API is called Toothpick, and they're an online dental booking agency. And so you can go online, you can book your dental appointment with your NHS dentist, and then they bake in transport information to tell you how to get there. So we have pubs using them, and screens so people can order their dinner, and then they say, "You've got 10 minutes till the next bus." So all sorts of cross-platform applications. >> That you never could've envisioned. >> Emer: Never. >> And it's just your point earlier about it's not a zero sum game, you're giving so many ways to create value. >> Emer: Right, right. >> Again, I come back to this notion of education and creativity in the United States education system, so unattainable for so many people, and that's a real concern, and you're seeing the middle class get hollowed out. I think the stat is, the average wage in the United States was 55,000 in 1999, it's 50,000 today. The political campaigns are obviously picking at that scab. What's the climate like in Europe from that standpoint? >> In terms of education? >> No, just in terms of, yes, the education, middle class getting hollowed out, the sentiment around that. >> I don't think people are up to speed with that yet, I really don't think that they're aware of the scale. I think when they think robots or automation, they don't really think software. They think robots like there were in the movies, that would come, as I say, and do those jobs nobody wanted. But not like software. So when I say to them, look, E-discovery software, when it's applied retrospectively, what it shows is that human lawyers are only 60% accurate compared to it. Now, that's a no-brainer, right? If software is 100% accurate, I'm going to use the software. And the ratio difference is 1 to 500. Where you needed 500 lawyers before you need 1. So I don't think people are across the scale of change. >> But it's interesting, you're flying to Heathrow, you fly in and out, you're dealing with a kiosk. You drive out, the billboards are all electronic. There aren't guys doing this anymore. So it's tangible. >> And I think, to your point about education, I'm not as familiar with the education system in the US, but I certainly think, in Europe and in the UK, the education system is not capable of dealing even with the latest digital natives. They're still structuring their classrooms in the same way. These kids, you know-- >> John: They have missed the line with the technology. >> Absolutely. >> So reading, writing and arithmetic, fine. And the cost of education is maybe acceptable. But they may be teaching the wrong thing. >> Asynchronous non-linear, is the thing. >> There's a wonderful example of an Indian academic called Sugata Mitra, who has a fabulous project called a Hole in the Wall. And he goes to non-English speaking little Indian villages, and he builds a computer, and he puts a roof over it so only the children can do it. They don't speak English. And he came back, and he leaves a little bit of stuff they have to get around before they can play a game. And he came back six months later, and he said to them, "What did you think?" And one of the children said, "We need a faster CPU and a better mouse." Now, his point is self-learning, once you have access to technology, is amazing, and I think we have to start-- >> Same thing with the non-linear consumption, asynchronous, all this, the API economy enabling new kinds of expectation and opportunities. >> And it was interesting because the example, some UK schools tried to follow his example. And six months later, they rang him up and they said, "It's not working," and he said, "What did you do?" And they said, "Well, we got every kid a laptop." He said, "That's not the point." The point was putting a scarce resource that the children had to collaborate over. So in order to get to the game, they had figure out certain things. >> I think you're right on some of these (mumbles) that no one's talking about. And Dave and I are very passionate on this, and we're actually investing in a whole new e-learning concept. But it's not about doing that laptop thing or putting courseware online. That's old workflow in a new model. Come on, old wine in a new bottle. So that's interesting. I want to get your thoughts, so a personal question to end this segment. What are you passionate about now, what are you working, outside of the venture, which is exciting. You have a lot of background going back to technology entrepreneurship, public policy, and you're in the front lines now, thought leading on this whole new wide open sea of opportunity, confusion, enabling it. What are you passionate about, what are you working on? Share with the folks that are watching. >> So one of the main things we're trying to do. I work as an associate with Ernst & Young in London. And we've been having discussions over the past couple of months around techno ethics, and I've basically said, "Look, let's see if we can get EY "to build to build an EY good governance index." Like, what does good governance look like in this space, a massively complex area, but what I would love is if people would collaborate with us on that. If we could help to draw up an ethical framework that would convene the technology industry around some ethical good governance issues. So that's what I'm going to be working on as hard as I can over the next while, to try and get as much collaboration from the community, because I think we'd be so much more powerful if the technology industry was to say, "Yeah, let's try and do this better "rather than waiting for regulation," which will come, but will be too clunky and not fit for purpose. >> And which new technology that's emerging do you get most excited about? >> Hmm. Drones. (laughter) >> How about anything with bitcoin, block chains? >> Absolutely, absolutely, block chain. Yeah, block chain, you have to say, yeah. I think, 'cause bitcoin, you know, it's worth 20 p today, it's worth 200,000 tomorrow. >> Dave: Yeah, but block chain. >> Right, right. I mean, that is incredible potentiality. >> New terms like federated, that's not a new term, but federation, universal, unification. These are the themes right now. >> Emer: Well, it's like the road's been coated, isn't it? And we don't know where it's going to go. What a time we live in, right? >> Emer Coleman, thank you so much for spending your time and joining us on theCUBE here, we really appreciate the conversation. Thanks for sharing that great insight here on theCUBE, thank you. It's theCUBE, we are live here in Dublin, Ireland. I'm John Furrier with Dave Vellante. We'll we right back with more SiliconANGLEs, theCUBE and extracting the signal from the noise after this short break. (bright music)