>>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

>> live from Boston, Massachusetts. It's the you covering your red have some twenty nineteen rots. You buy bread hat. >> Good to have you back here on the Cube as we continue our coverage. Live at the Red Had Summit twenty nineteen, Day three of our coverage with you since Tuesday. And now it's just fresh off the keynote stage, joining stew, Minutemen and myself. Chris. Right? VP and chief technology officer at Red Hat. Good job there, Chris. Thanks for being with us this morning. Yeah. >> Thank you. Glad to be here. >> Great. Right? Among your central things, you talked about this, this new cycle of innovation and those components and how they're integrating to create all these great opportunities. So if you would just share for those with those at home who didn't have an opportunity to see the keynote this morning, it's what you were talking about. I don't think they play together. And where that lies with red hat. Yeah, you bet. >> So, I think an important first kind of concept is a lot of what we're doing. Is lane a foundation or a platform? Mean red hats focuses in the platform space. So we think of it as building this platform upon which you build an innovate. And so what we're seeing is a critical part of the future is data. So we're calling it a Kino data centric. It's the data centric economy. Along with that is machine learning. So all the intelligence that comes, what do you dividing? The insights you're grabbing from that data. It introduces some interesting challenges data and privacy and what we do with that data, I mean, we're all personally aware of this. You see the Cambridge Analytica stuff, and you know, we all have concerns about our own data when you combine all of us together techniques for how we can create insights from data without compromising privacy. We're really pushing the envelope into full distributed systems, EJ deployments, data coming from everywhere and the insights that go along with that. So it's really exciting time built on a consistent platform like lycopene shift. >> So, Chris, I always loved getting to dig in with you because that big trend of distributed systems is something that you know we've been working on for quite a long time. But, you know, we fully agree. You said data at the center of everything and that roll of even more distributed system. You know, the multi cloud world. You know, customers have their stuff everywhere and getting their arms around that, managing it, being about leverage and take advantage. That data is super challenging. So you know where where, you know, help us understand some of the areas that red hat in the communities are looking to solve those problems, you know, where are we and what's going well and what's still left to work on. >> Well, there's a couple of different aspect. So number one we're building these big, complex systems. Distributed systems are challenging distribute systems, engineers, air really solving harder problems. And we have to make that accessible to everybody operations teams. And it's one of the things that I think the cloud taught us when you sort of outsource your operations is somebody else. You get this encapsulated operational excellence. We need to bring that to wherever your work clothes are running. And so we talked a lot about a I ops, how you harness the value of data that's coming out of this complex infrastructure, feed it through models and gain insights, and then predict and really Ultimately, we're looking at autonomic computing how we can create autonomous clouds, things that really are operating themselves as much as possible with minimal human intervention. So we get massive scale. I think that's one of the key pieces. The other one really talking about a different audience. The developers. So developers air trying to incorporate similar types of intelligence into their applications were making recommendations. You're tryingto personalize applications for end users. They need easy access to that data. They need easy access to train models. So how do we do that? How do we make that challenging data scientist centric workflow accessible to developers? >> Yeah, just some of the challenges out there. I think about, you know, ten, fifteen years ago, you talk to people, it was like, Well, I had my central source of truth and it was a database. And you talk to most companies now and it's like, Well, I've got a least a dozen different database and you know, my all my different flavors of them and whether in the cloud or whether I have them in my environment, you know, things like a ops trying to help people get involved with them. You talked a little bit in your keynote about some of the partners that you're working on. So how do you, you know, bring these together and simplify them when they're getting, you know, even more and more fragmented? >> Well, it's part of the >> challenge of innovation. I mean, I think there's a there's a natural cycle. Creativity spawns new ideas. New ideas are encapsulated in projects, so there's a wave of expansion in any kind of new technology time frame. And then there's ultimately, you see some contraction as we get really clear winners and the best ideas and in the container orchestration space communities is a great example of that. We had a lot of proliferation of different ways of doing it. Today we're consolidating as an industry around Cooper Netease. So what we're doing is building a platform, building a rich ecosystem around that platform and bringing our partners in who have specific solutions. They look at whether it's the top side of the house, talking to the operations teams or whether it's giving developers easy access to data and training models through some partners that we had today, like perceptive labs and each to a A I this partnership. Bringing it to a common platform, I think, is a critical part of helping the industry move forward and ultimately will see where these best of breed tools come into play. >> Here, uh, you know, maybe help a little bit with with in terms of practical application, you got, you know, open source where you've got this community development going on and then people customized based on their individual needs all well, great, right? How does the inverse happen? Where somebody who does some custom ization and comes up with a revelation of some kind and that applies back to the general community. And we can think of a time where maybe something I'm thinking like Boston children, their imaging, that hospital we saw actually related to another industry somehow and gave them an ah ha moment that maybe they weren't expecting an open source. Roy was the driver that >> Yeah, I think what we showed today were some examples of what If you distill it down to the core, there's some common patterns. There's data, they're streaming data. There's the data processing, and there's a connection of that processed data or train model to an application. So we've been building an open source project called Open Data Hub, where we can bring people together to collaborate on what are the tools that we need to be in this stack of this kind of framework or stack And and then, as we do, that we're talking to banks. They're looking at any money laundering and fraud detection. We're talking to these hospitals that were looking at completely different use cases like HC Healthcare, which is taking data to reduce the amount of time nurses need to spend, gathering information from patients and clearly identify Septus sepsis concerns totally different applications, similar framework. And so getting that industry level collaboration, I think is the key, and that having common platforms and common tools and a place to rally around these bigger problems is exactly how we do that through open source. >> So Lynn exits and an interesting place in the stack is you talked about the one commonality and everything like that. But we're actually at a time where the proliferation of what's happen to get the hardware level is something that you know of an infrastructure and harbor guy by background, and it was like, Oh, I thought We're going to homogenize everything, standardize everything, and it's like, Oh, you're showing off Colin video stuff. And when we're doing all these pieces there, there's all these. You know, new things, Every been things you know you work from the mainframe through the latest armed processors. Give us a little insight as to how your team's geeking out, making sure that they provide that commonality yet can take advantage of some of the cool, awesome stuff that's out there that's enabling that next wave of innovation. >> Yeah, so I share that infrastructure geek nous with you. So I'm so stoked the word that we're in this cycle of harbor innovation, I'll say something that maybe you sounds controversial if we go back in time just five years or a little, a little more. The focus was around cloud computing and bringing massive number of APS to the cloud, and the cloud had kind of a T shirt size, small, medium, large view of the world of computer. It created this notion that Khun computers homogenous. It's a lie. If you go today to a cloud provider and count the number of different machine types they have or instance types it's It's not just three, it's a big number. And those air all specialized. It's for Io throughput. It's for storage acceleration. It's big memory, you know. It's all these different use cases that are required for the full set of applications. Maybe you get the eighty percent in a common core, but there's a whole bunch of specific use cases that require performance optimization that are unique. And what we're seeing, I think, is Moore's law. The laws of physics are kind of colliding a little bit, and the way to get increased acceleration is through specialized hardware. So we see things like TP use from Google. We see until doing deal boost. We've got GPS and even F p G A s and the operating system is there TIO give a consistent application run time while enabling all those hardware components and bringing it all together so the applications can leverage the performance acceleration without having to be tied directly to it. >> Yeah, you actually think you wrote about that right now, one of your a block post that came about how hardware plays this hugely important role. You also talked about innovation and change happening incrementally and And that's not how we kind of think about like big Banks, right? Yeah. Wow, this is But you pointed out in the open source, it really is step by step by step. Which way? Think about disruption is being very dramatic. And there's nothing sexy about step by step. Yeah, that's how we get to Yeah, disruption. I kind of >> hate this innovation, disruption and their buzz words. On the one hand, that's what captures attention. It's not necessarily clear what they mean. I like the idea that, you know, in open source, we do every day, incremental improvements. And it's the culmination of all these improvements over time that unlock new opportunities. And people ask me all the time, where is the future? What do we do and what's going on? You know, we're kind of doing the same thing we've been doing for a long time. You think about micro services as a way to encapsulate functionality, share and reuse with other developers. Well, object oriented programming decades ago was really tryingto tryingto established that same capability for developers. So, you know, the technologies change we're building on our history were always incrementally improving. You bring it all together. And yes, occasionally you can apply that in a business case that totally disrupts an industry and changes the game. But I really wanted encourage people to think about what are the incremental changes you can make to create something fundamentally new. >> All right, I need to poke it that a little bit, Chris, because there's one thing you know, I looked back in my career and look back a decade or two decades. We used to talk about things like intelligence and automation. Those have been around my entire career. Yeah, you look it today, though, you talk about intelligence and talk about automation, it's not what we were doing, you know, just the amount of degrees, what we're having there. It is like if we'd looked at it before, it was like, Oh, my gosh, science fiction's here so, you know, way sometimes lose when we're doing step by step, that something's there making step function, improvements. And now the massive compact, massive changes. So love your opinions there. >> Yeah, well, I think it's a combination, so I talk about the perpetual pursuit of excellence. So you pick up, pick a field, you know, we're talking about management. We got data and how you apply that data. We've been working towards autonomic computing for decades. Concepts and research are old, the details and the technologies and the tools that we have today are quite different. But I'm not. You know, I'm not sure that that's always a major step function. I think part of that is this incremental change. And you look at the number for the amount of kind of processing power and in the GPU today No, this is a problem that that industry has been working on for quite a long time. At some point, we realize, Hey, the vector processing capabilities in the GPU really, really suit the machine learning matrix multiplication world real world news case. So that was a fundamental shift which unlocked a whole bunch of opportunity in terms of how we harness data and turn it into knowledge. >> Yes. So are there any areas that you look at? Now that we've been working at that, you feel we're kind of getting to those tipping points or the thie waves of technology or coming together to really enable Cem Cem massive change? >> I do think our ability to move data around, like generate data. For one thing, move data around efficiently, have access to it from a processing capability. And turning that into ah, >> model >> has so fundamentally changed in the past couple of decades that we are tapping into the next generation of what's possible and things like having this. This holy grail of a self healing, self optimizing, self driving cluster is not as science fiction as it felt twenty years ago. It's >> kind of exciting. You talk about you've been there in the past, the president, but there is very much a place in the future, right? And how would that future looks like just from from again? That aye aye perspective. It's a little scary, sometimes through to some people. So how are you going about, I guess, working with your partners to bring them along and accept certain notions that maybe five six years ago I've been a little tough to swallow or Teo feel comfortable with? >> Yeah, well, there's a couple of different dimensions there. One is, uh, finding tasks that air computers are great at that augment tasks that humans were great at and the example we had today. I love the example, which was, Let's have computers, crunch numbers and nurses do what they do best, which is provide care and empathy for the patients. So it's not taking the nurse's job away. In fact, is taking the part that is drudgery ITT's computation >> and you forget what was the >> call it machine enhanced human intelligence right on a couple of different ways of looking at that, with the idea that we're not necessarily trying to eliminate humans out of the loop. We're trying to get humans to do what they do best and take away the drudgery that computers air awesome at repetitive tasks. Big number crunching. I think that's one piece. The other pieces really, from that developer point of view, how do you make it easily accessible? And then the one step that needs to come after that is understanding the black box. What happens inside the machine learning model? How is it creating the insights that it's creating and there's definitely work to be done there? There's work that's already underway. Tto help understand? Uh, the that's really what's behind the inside so that we don't just trust, which can create some problems when we're introducing data that itself might already be biased. Then we assumed because we gave data to a computer which is seemingly unbiased, it's going to give us an unbiased result, right? Garbage in garbage out. >> So we got really thoughtful >> about what the models are and what the data is that we're feeding >> It makes perfect sense it. Thanks for the time. Good job on the keynote stage again this morning. I know you've got a busy afternoon scheduled as well, so yeah, I will let you. We'Ll cut you loose. But thank you again. Always good to see you. >> Yeah. I always enjoy being here >> right at that's right. Joining us from red hat back with Wharton Red Hat Summit forty nineteen. You're watching live here on the Cube?

>> From Gillette Stadium in Foxboro, Massachusetts, if the queue recovering Vita Winter warmer, twenty nineteen Brought to you by Silicon Angle media. >> Hi. I'm stupid men. And this is the cubes coverage of V tug Winter warmer. Twenty nineteen here. A Gillette Stadium, home of the New England Patriots. Happy to welcome to the program. A community member, Someone I've known for many years at this point. Jonathan Frappe here. Who's with V Brown bag? Thanks so much for joining us from >> Thanks for having me. >> All right, so, you know, I watched this event, and when it started, it was, you know, originally the V mug for New England. And then it became vey tug And one, there's some of the politics stuff which we don't need to go into, but part of it was virtual ization and cloud. And what's the interaction there and what will users have to do? Different. And part of that is jobs. And one of the reasons I really wanted to bring you on is, you know, you started out heavy in that virtual ization base and you've been going through those machinations. So maybe just give our audience a little bit about, you know, your background, some of the things skill sets. You've got lots of acronyms on your on your you know, resume as it is for certification. You've done. So let's start there. >> Sure. So my background. I started this help desk. I did Windows two thousand Active Directory, administration and Exchange Administration all on site and moved into Mohr server administration. And when the empire started to become a thing, I was like, Wow, this is This is a game changer and I need to sort of shift my skill set. I understand the applications of music. I've been supporting him. But virtualization is going to change change That so started to shift there and saw a similar thing with Public Cloud and automation a cz, That same sort of next step beyond infrastructure management. >> All right. And you've had a bunch of certification. The real off a few. You know what? Where are you today? What? What have you added gives a little bit of a timeline. >> My first certification was a plus which come to you seemingly has come around and joined the ranks of posting toe linked in for everybody. So a plus was my first one. EMC PM, CSC on Windows two thousand. Took a little bit of a break in back into it. Bcp five era so four, five years ago. Cem Cem. Other of'em were Certs NSX Cloud see Emma and most recently, the solution's architect associate for a Ws. >> OK, great, in when you look at the kind of virtual ization and cloud, it's not like you thirst, which one day and said, Okay, I no longer need the VM were stuff. I'm going to do the cloud tell us a little bit about you know what led you to start doing the cloud and you know how you you know how your roles that you've had and you know the skill set that you want to have for your career. You know how you look at those. >> So for me, it is about being able to support what my business is doing. And sometimes the right answer's going to be VM, where sometimes it's going to be physical. Sometimes it's going to be containers or public cloud, or, you know, new fancy buzzwords like server lists. And I've always in my career tried to support what where, what application we're delivering to get the business, the information they need. So for me to do that properly, I need to be well versed across all of that infrastructure so that when when it's time to deliver something in public cloud or time to deliver something in the container, I'm ready to go when you do that. >> Yeah. What? What? What's the push and pull for some of the training bin? Is this something that you've seen? You said, like Veum, where you saw it, like, Oh, my gosh, I need to hop on that. You know, I remember back to those early days I remember engineers I worked with that were just like, this thing is amazing. That was like preview motion, even. Yeah, but you know, just what? That that impact we've seen over the last, you know, ten to fifteen years of that growth has there been times where the business is coming said, Hey, can you go learn this? Kaixian orders have been you driving most about yourself. Uh, >> it's it's been both. There are times when the business has come and said, Hey, we would really like to take advantage of virtual ization or public cloud. And it from a technology perspective, there may have been other factors that would impact the ability to do that. So that's why for me. I tried to sort of stay ahead of it when, you know virtual ization was taking off and everything I had was on physical servers. I knew I needed to have the VM where peace in my pocket so that when the business was ready and when other things like compliance, we're ready for it. We could move forward and sort of advanced that same thing with Public Cloud. Now that that's Mohr prevalent and sort of accepted in the industry a lot more cos they're moving in that direction. >> Yeah, and you know, what tips would you give your Pierre if they're a virtual ization person? You know, how are the waters in the cloud world is there are a lot of similarities. Is it? You know, do I have to go relearn and, oh, my gosh, I need to go learn coding for two years before I understand how to do any of this stuff. >> I think it's helpful. Tto learn some level of coding, but do it in an environment that you're comfortable in today. So if you're of'em were admin today, you know there's power, see Ally and be realized orchestrator and and even if you're on via Mars Cloud platform there's there's some basic power shell on bass scripting you could do in the cloud Automation. Get comfortable with the environment, you know. And then as that comfort grows when you move Oh, look, there's power shell commandments for a ws. If that's the route, you go so oh, already understand the format and how I how I glue those things together so you could get comfortable in the environment you're in today and sort of get ready for whatever that next step is. >> Yeah, I've always found I find it interesting. Look at these ecosystems and see where the overlaps and where two things come together. You know, I actually worked with Lennox for about twenty years. So I you know, back when I worked at Emcee the storage company and I supported the Lenox Group and Lennox was kind of this side thing. And then you kind of saw that grow over time and Lennox and virtual ization. We're kind of parallel, but didn't overlap is much. And then when we get to the cloud, it feels like everybody ended up in that space and there were certain skill sets that clinics people had that made it easy to do cloud in certain things that the fertilization people had that made it easy do there. But we're kind of all swimming in the same pools. We see that now in the, you know, core bernetti space. Now I see people I know from all of those communities on, but it's kind of interesting. Curious if you have anything you've seen in kind of the different domains and overlapping careers. >> Yes, you. For me. I think what's help is focusing on how the applications the business uses consumed, what some of the trends are around, how you know whether finance or marketing teams are interacting with those applications. If I know how the application works and what I need to do something to support it, the concepts aren't going to be vastly different. If I know how Exchange's install their sequel servers install, there's some custom application is insult. I could do that across the VM, where environment native US environment and should it supported into Docker by leveraging Cooper Netease. >> All right, so you've mentioned about the time the application, can you? How has it changed your relationship with kind of the application owners as you go from, you know, physical, virtual, the cloud. >> I don't think it should change much. The problem probably the biggest shift that you have is that at some point now, things are out of your control. So when I've got a server sitting in my data center that I can walk down the hallway to if something's not working, I have access to it. If there's an application down in the public cloud, or there's an A Ws outage or any public cloud provider outage, I have to wait. And that sort of I think the thing that I've seen business struggle with the most like, well, it's down, go fix it. It's like, I can't get to it right now, and I'm probably not driving to Virginia, Oregon to go reboot that server for Amazon. >> Whoever absolutely big shift we've seen right is, you know a lot of what I is. It I am managing is now things that aren't in my environment. You know, there was my data centers. My might have had hosted data centers where I'd call somebody up, you know, you know, tell the Rex paper person to reboot the servers or it's right, it's in the public cloud. In which case it's like, OK, what tools. What can I trouble shoot myself? Or is there some, you know, out of that I'm not aware of, you know, is affecting me. Yeah, >> it's Ah, it's a good shift to have for a infrastructure person because we're really getting to the point now. I think the tails, the scales have tipped to focusing more on delivering business value versus delivering infrastructure. The CFO doesn't necessarily think or care that spinning up a new V m faster is cool. They care about getting their application to their team so that they could do their work. So I think taking, you know, going to public cloud or going to other platforms where that's removed it sort of forces you to move to supporting supporting those business applications. >> So I'm curious it every time we have one of these generational shift time. Time is like, Oh, my gosh, I'm going to be out of a job on the server ID men Virtualization is going to get rid of me. I'm a virtual ization Had been cloud's going to get rid of me. This whole server listing will probably just get rid of all the infrastructure people I've read article yesterday was called the Creeping Apocalypse a CZ what they called it. But, you know, you know what you saying is there general fear in your peers or, you know, do you just, you know, dive in and understand it and learn it? If you could stay, you know, up with or a little bit ahead of the curve, you know you're going to keep employed. >> I would say that there's a mix there. Some people, even just a few months ago, some some folks I talked to and they were just sort of breaking into automation and like how they can automate deploying their applications in their legitimate concern, was I won't have a job anymore and sort of the way I looked at that was my job's going to change. I don't spend my entire day administering Windows two thousand active directory boxes any more. So I need Yes, I need to shift that and start thinking about what's next. If I can automate the routine task, you know, deploying an application, patching and application, bringing things up and down when there's some sort of failure than I, uh, I'm going to naturally grow my career in that way by getting rid of the boring stuff. >> Yeah, and I've been here in this argument against automation for decades now, and the question I always put two people is like, Look, if I could give you an extra hour a day or an extra day a week, do you have other projects that you could be doing or things that the business is asking for? That would be better. And I've yet to find somebody that didn't say, Yeah, of course, on DH. What are the things that you're doing that it would be nice to get rid of, You know, other people is like I love the serenity of racking and stacking cabling stuff. And nothing gets people more excited than beautiful cables in Iraq. I thought yesterday I saw people like going off about here's this data center with these beautiful, you know, rack, you know? So with the cable ties and everything, but I'm like, really, you know, there's more value you can add absolutely out there. So >> automate yourself into your next job. It is sort of the way I think I like to think about it. It's not a meeting, >> so let's you know, just look forward a little bit, you know? There's all these waves, you know, Cloud been a decade data was talking to keep downs in this morning on the Cube on we said, you know, when he talks to users, it's their data that super important applications absolutely is what drives, uh, you know, my infrastructure, but it's the data that's the super important piece. So you know, whether it be, you know, you're a I or, you know, you figure various buzz word of the day I ot You know, data is in the center. So what do you looking forward to is? Are there new search or new training that that are exciting? You are areas that you think you're Pierre should be poking out to help try to stay ahead of the curve. >> Yeah, and back to my earlier point about leveraging the thing you know today and how to sort of grow your career. And that next skill set is how I can look at data and make. I understand what's going on around that. So maybe maybe today that's taking some stats from any SX. I hosted an application and correlating that data together on help. You underst Yes. And you know what that means for the applicator action before or use their calls in. And that's going to help you grow into sort of this new realm of like, machine learning and big data. And in analytics, which I think is really the next thing that we're going to need to start doing as Mohr and more of that infrastructure shifted away into surveillance platforms and things that were not worried about How can I understand? How can I take that data? Transform it, use it, correlated together to, you know, help make decisions. >> Alright, on final thing, give us update on our friends at V Brown bag. So, you know, we talked Well, I always say, you know, when we go to V m world, it's like we're there. I'm trying to help kind of balance between the business and the technology. You want to go a little deeper and really geek out and understand some of these things. That's where you know the V brown bag. You know, people are going to be able to dig in with the community in the ecosystem. There was the V and V brown bag for virtual ization. But he brown bags doing much more than just traditional virtualization today. You know what? What? What's on the docket? >> Eso upcoming This year, we're gonna have some episodes around Python so helping add men's get to know Python start to get comfortable with it, Which would be a great language to a automate things that maybe you're doing today in your application, but also to be able to take data and and use Python, too. Manage that data extract value out of that data so that you can help make decisions. So look for the throughout this year and, you know, learn new things. >> All right, Jonathan, from pure pleasure to talk with you on camera after talking to off camera for many years. Thanks so much for joining us. All right. And we appreciate you joining us at this virtual ization and cloud user event. Ve tug Winter warmer. Twenty nineteen on student a minute. Thanks for watching the cue

>> From Seattle. Washington. It's the key you covering Goob Khan and Cloud Native Con North America. Twenty eighteen. Brought to you by Red Hat, the Cloud Native Computing Foundation and its ecosystem partner. >> Okay. Welcome back. It runs the cubes. Live coverage of three days Wall to wall here at Koop Khan and Cloud Native Khan, twenty eighteen in Seattle, where day three only actions happening. Mr Keep John for was to Minuteman where you have bread. French Compton, Tina. Director, Technical Market had read, had breaking down the container storage trends and directions. Costly containers are super important. That's happened. Communities has happened. Now. New things were happening around a lot of innovation. Thanks for coming on the Q. Appreciate it. >> Thanks for having me back. >> So what's the state of the art of containers of trends? Some of the market directions? What's going on around containers? >> Well, here at this show, of course, it's been all about service mesh. Right is Theo. Service mesh, dynamically dynamic discovery, dynamic invocation of services. But all of those things Well, a certain percentage of those things, according to Keynote, require some type of persistent so eso yet service message, service meshes and persistence. >> So storage is a big part of the networking and compute all working together. The cloud that's been a big part of it. What's what's important here in this show? What's going on this week. That's really impacting that piece of it. That container in storage you mentioned state versus stateless work area stateless is to find people from persistence in state become important and applications. How much conversation's been here this week on that piece >> we'll talk about this week, and then I'll talk about the last couple of weeks this week. There, there. Couple of significant thing is going on. They're going to sort of unleash innovation in persistence as it pertains to the coup bernetti subsystem. First, of course, is a container storage inter. See, you know, today, all the all of the volume plug ins have been entry. You want to change. You know, some vendor wants to change their their storage capabilities. They need to re compile the binaries. Very slow. Very, very non agile. Of course, with the advent of the container storage interface, it's okay. Here's the common interface. All the all the volume plugging providers right to that interface so they could. Then they Khun Iterated to their heart's content without having to change the the entry >> source. So the impact is what? Speed, agility, >> agility of innovation, allowing all those guys t innovate Kind of the second thing. That's so that's man of discussion this week. Another thing's been a discussion you've seen in the in some of the sessions and stuff is the operator framework, you know, coming a champion by the Coral West guys, of course. Now part of Red hat, the operator framework in terms of effectively automating things that human operators would do for complex subsystems. Such a CZ storage. Eso basic installation based basic upgrades, you know, monitoring those services. So when you know something falls over, what do you do with that type of stuff? So I'd say C s I container storage interface as well as operator from me. Those are some of the things have been talked about this week. I still want to go back. Talk about last week, but go ahead. >> I wonder if you could tease this out a little before. So, you know, lost five years. You know, container ization, Cooper Netease. You know, massive change the way we think about architectures. Things like networking in storage. I have often been the anchor to kind of hold us down to be ableto make changes faster. Virtual ization helped some, but you know, container ization. We're gonna have to fix some of these same things. What conversations you're having with customers, You know, give us the latest on the, you know, the state versus state falls we heard in the keynote. It was They said forty percent of deployments have, you know, st full applications out there spending on numbers. And, you know, it's definitely has been growing. And at least I can do it as opposed to, you know, two years ago, it was like, Okay, we're doing containers, but we're just going to stateless for now, and we'll try to figure out what architectures goingto work. Even a year ago at this show, I heard in the back rooms there were lots of arguments as to which one of the storage projects was going to lead and seems seems like we're getting some maturity. I hope we hope to give us some visibility is where we are, and you know what's working and what still needs to be done. >> So although the industry talks about serve earless there, not yet talking about data lists, the or storage lists. I mean, you know, if we threw out the basic principle of data gravity data is the sun around which applications services rotate And so even I mean, even stateless aps stateless app Still do I owe frequently? The io of stateless apse is, you know, be arrest Will puts and gets to an object store that actually brings me. So let's let's talk about let's unpack the stateless and then let's go to St ful. So I'm gonna come back. Tio some of the conversations. A couple of weeks ago, Red had announced the acquisition of Nuba and Israeli Company. So when you think about what new Bob Plus sef due to provide stateless aps with a common set of Davis, a common set of David data services across the hybrid and multi cloud so those stateless app saying, Okay, I'm going to do I'm going to rest well puts and gets. But, man, it's complicated. If I'm gonna have to develop to various proprietary protocols I've got, you know, the is your blob protocol. I've got a W. S s three. I'm talking Teo Google persistent disc. And then if I want to run hybrid, I'm also talking to SEF objects storage on premises. And if I'm a developer I'm thinking, man, Wouldn't it be nice if I had a common set of David data services, including common protocol to talkto all of those different cloud storage back end? So, Nuba some people kind of call it a cloud storage controller provides that kind of common data services. So things like common FBI protocol? Um, things like mirroring. So you you want to write, right Once you're uprights once and it smeared across the various cloud object storage back ends to facilitate easy migration. The second one I wanna uproot to move over here. Your data is already there. So that's, uh that's a couple of reasons. And some of the conversation from a couple weeks ago about how Nuba plus self are helping stateless aps get Teo hybrid and multi cloud >> this. I think that is a great point. You have a hybrid cloud and multi cloud coming around the corner, which is about choice, Right? But see, I CD pipe lining of having a consistent developer environment clearly is one of the main benefits we're seeing in this community here. Okay, I got some sulfur developers with crank teams move around that consistency, no matter where were the environment is just really good goodness. Their storage is interesting and data is that because you're right, the sun is the data and every is orbiting around it. That's the Holy Grail. This is what people want. They want addressable data. They wanted real time. They wanna have an access. They don't want to do all this code to configure manage data, and it's complicate. Got data warehouses? You got time. Siri's data so date is getting more complicated, but it needs to be simple. So this is kind of challenge of the industry. How are you guys seeing that with open ship? How is your container piece fit in? How do you guys make that easy for customers to say? Look, I want to have that data like I wanted intelligent, that brick of access to data. So my abs don't have to do all the heavy lifting almost like Dev ops for data. It's like day tops, like I need to have programmable data on the absolutely which, which have thoughts on that. >> So first I wanna I wanna address that in two ways. The first is about open shipped itself that what you described is in fact, the sweet spot of what open shift is providing a common set of Cooper Nettie Services. Plus. See, I see the pipeline services for developers and operation staff independent of your cloud infrastructures. So whether open shift is running on top of a heavy west, whether it's running on top of his your whether it's running on top of the G, C. P. Whether it's running on premises on bare metal, you know, common set of cou bernetti services and CD pipeline services. Okay, that so what you described there's wanted to just highlight that That is open ship hybrid multi >> valuable check. That's awesome data >> now coming down. Coming down to data. So, in fact, open shift container storage is the mirror analog to open shift for that, providing a common set of Cooper Netease volume services. Independent of what? The storage substrate. ID. So think about it. If you're If you're inside of eight of us, you've got CBS is what's you know? When in Rome, act as the Romans. You've got E. B s there when you're inside of eight of us. Well, the type of communities volumes service of the CBS provides natively differed them for instance, when you're on premises and it's surfacing via and NFS plug in, maybe different. Likewise. We're inside of a CZ. You're with your persistent disco, so open shift container storage device the same type of abstraction Lee are providing a common set of cou bernetti communities volumes services independent of what? The storage server layer is so >> cool you guys was tracked away the complexity. So the APP developer doesn't do anything about storage on those discreet platforms, >> doesn't know anything about storage and provides a common set of services instead of Well, let's see, this is running on this cloud. I don't have the have a different set of services, so common set of services. >> So one of things I love about talking right out of the shows is you actually have a lot of customers that are doing this way. Actually, we spoke to one of your customers yesterday. Talk about how you know communities is helping them create sustainable data centers over in Europe. In the Nordics, especially so communities is awesome. But what's really awesome is the things that we can do on top of it. I wonder if you've got, you know, help connect some of this toe. You know, your customers really things, you know? How does this, you know, change the game? How does it change their teams? You know, what can you share with us? >> One of things that I can't. What's what's top of mind. So what's not top of mind for me at the moment is you know what kind of knew how their reinventing the world what is top of mind with me right now? We've just been studying. Our our results is we look back and this is a little bit of a A Okay? It's a trend, but it's a different kind of friend you're talking about. In the last six quarters, we've had six hundred percent growth with open ship container storage. Um, so And now we send last six quarters were also at a point. Now we're seeing some of those same folks from the Nordics here. You're describing that are coming back now, you know, they have experimented on, So there are some There are Cem Cem cruise ship. There's a cruise ship company that is deployed this on on ships. What we're now seeing. What's very gratifying for us is they're coming back now for a second pass. Now, a year into it, it's okay. Clearly, it must be providing enough value that you come back. Okay. I want to buy this for another ship or more shifts. That's gratifying for us. The first year was, let's see. Let's try this uber Netease, this open ship container store stuff out. But, you know, coming back to the trough for another take, It's good for us. >> And what's going around the corner? He opens shifting, doing great. I love this abstraction layer we're seeing for the first time in the industry, clear visibility and a real value proposition. When I were joking yesterday, you know, we were at open stack years ago, or even Cube con three years ago. We would ask the question If you had a magic wand, what would you hope to have happened? It's actually some of the things that are actually happening. I mean, clean, heavy lifting is gone, and all the developer side consistency, productivity, better advantage on the application development side and then taking away all the hassles of having that she trained people on multiple clouds. So this is kind of happening. What's next? So what's the next next, uh, bowling pin to fall down? What's the, you know, Hit the front ten. What's next? What's going on? How do you guys see the next innovation around Open ship and storage containers, >> cloud independent data services and mobility. So independent of the clouds. And again, it's hybrid, too. So you don't want to be locked into your own cloud either. So cloud independent data services and mobility. So he said, Listen, I want to be I want to have a common de doop compression mirroring, but I want to sit in the layer above my clouds back to the data gravity thing. I want to ensure that my data is where I need it on different clouds. So I'm elevating to a new layer this this cloud storage controller, this this cloud independent set of data services way. Think that's where the pucks going? >> Yeah, I think the data date is critical, I think. Way said years ago. Data ops. There's a Dev ops model for data. You look at that way's not just putting into a data lake actually making it useful. Yeah, Thanks. Come on. Cuba. Here. Bringing all the data here. The Cube. We're sharing it here. Live in Seattle. Is our third year coop coming there from the beginning? That's the cubes coverage of Cloud Native Khan and Coop gone. Bring all the action here. Was red hot on the Cube. Back with more live coverage. Stay with us. Day three, three days ago off the wall. Coverage will be back after this short break.