 From around the globe, it's theCUBE presenting Adaptive Data Governance. Brought to you by IO Tahoe. Our next segment here is an interesting panel. You're gonna hear from three gentlemen about Adaptive Data Governance. We're gonna talk a lot about that. Please welcome Yusuf Khan, the Global Director of Data Services for IO Tahoe. We also have San Diego Caster, the Chief Data Officer at the First Bank of Nigeria, and Gudran Vanderval, Oracle's Senior Manager of Digital Transformation and Industries. Gentlemen, it's great to have you joining us in this panel. Thank you. Great to be here. Thanks for having me. All right, San Diego, we're gonna 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. So, First Bank of Nigeria was created 125 years ago. It's one of the oldest, it's not the oldest bank in Africa. And because of the history, it grew everywhere in the region and beyond the region. I am currently based in London, where it's kind of the European 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 the Middle East, and yes, and it's a very kind of active banking in all these regions. So, San Diego, 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 of adaptive data governance because it's kind of, 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 used to work in consulting for 16, 17 years before joining the First Bank of Nigeria. And I saw many organizations trying to apply different type of approaches in data governance. And the beginning, early days, was really kind of a Yirashika way, a top-down approach where data governance was seen as implement a set of rules, policies, and procedures, but really from the top down. And it's important, it's important to have the battle of your C level, of your director, but whatever is so just that way, it fails. You really need to have a complementary approach. I often say, bottom up, and actually, as a CEO, I'm really trying to decentralize data governance to 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. And what you need to do is every business area needs information in order to take the decisions to actually be able to be more efficient or create value, et cetera. Now, depending on the business questions they have to solve, they will need certain data sets. So, they need actually to be able to have data quality for their own purpose. Now, when they understand that, they become the stewards naturally of their own data sets. And that is where my bottom line is nipping my top down. You can guide them from the top, but they need themselves to be also empowered and be actually in a way flexible to adapt the different questions that they have in order to be able to respond to the business needs. And I think that is where this adaptive data governance starts because if you want, I'll give you an example in the bank, we work, imagine a band diagram, right? So we have information that is provided to finance and all information to risk and all information to business development. And in this band diagram, there is going to be parts of that every circle that are going to kind of intersect with each other, right? So what you want as a data governance is to help providing what is in common and then let them do their own analysis to what is really related to their own area. As an example, nationality, you will say in a bank that when you open an account is the nationality of your customer. That's fine for finance when they want to do a balance sheet and accounting or a PNL. But for risk, they want that type of analysis plus the nationality of exposure, meaning where you are actually exposed as a risk. So you can have a customer that onboard in the UK but then trade with Africa and in Africa exposing their credit. So what I'm trying to say is they have pieces like pieces in common and pieces that are different. Now, I cannot impose a definition for everyone. I need them to adapt and to bring their answers to their own business question. That is adaptive data governance. 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 metadata classification in a very sophisticated way that you can actually create analytics 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, et cetera. But you will need to understand which areas need, what type of nationality or classification, which others will need that all the time. And the more you create that understanding that intelligence about how your people are using your data, you create in a way, big building blocks like a label, if you want, where you provide them with those definitions, those catalogs, you understand how they are used, but you let them compose like label. They would play their way to build their own analysis and they will be adaptive. And I think the new technologies are allowing that. And this is a real game changer, I was saying data governance. So one of the things that you just said, Sanjay would kind of struck me to enable the users to be adaptive. They probably don't want to be logging in support tickets. So how do you support that sort of self-service to meet the demand of the user so that they can be adaptive? Yeah, that's a really good question. And that goes along with that type of approach I was saying where in a way more and more business users wants autonomy and they want to basically be able to grab the data and answer as their own question. And when you have that, that's great because then you have demand. The business is asking for data, they are asking for the insights. So 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, of course, technology is one of the biggest winners without technology, we couldn't have been working remotely without these technologies where people can actually log in from their homes and still have a market data marketplace where they self-service 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 and we need to adapt and we need to be flexible. How do we do that with data? As a good example is every country, every government is publishing every day the stats of what's happening in their countries with the COVID and the pandemic. So they can understand how to react because this is new. So you need facts in order to learn and adapt. Now, the companies are the same. 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 and innovate and try to create responses. So that type of self-service of data, hundred for data, in order to be able to understand what's happening when the context is changing is something that is becoming more of the topic today because of the pandemic, because of the new capabilities, the technologies that allow that. And then you then are allowed to basically help your data citizens, I call them in organization, people that know their business and can actually start playing and answer their own questions. So these technologies that gives more accessibility to the data, that gives some cataloging. So they can understand where to go and what to find that gives lineage and relationships. All this is basically the new type of platforms and tools that allow you to create what I call a data marketplace. So once you create that marketplace, they can play with it. And I was talking about new culture and I'm going to finish with that idea. I think these new tools are really strong because they are now allowing for people that are not technology or IT people to be able to play with data because it comes in the digital world they are used to. I give you an example. Without your taco, 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 and look 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 tools. Now similarly, that data marketplace is in your taco you can for example, see which data sources are mostly used. So when I'm doing an analysis, I see that colleagues in my area are also using these sources. So I trust those sources. It's a little bit like Amazon. When you buy that, it suggests you what next to buy. Again, this is the digital kind of culture where people very easily will understand. Similarly, you can actually like some type of data sets that are working. That's Facebook. So what I'm trying to say is you have some very easy user friendly technologies that allows you to understand how interactive they are. And then within the type of digital knowledge that you have, be able to self serve, play, collaborate with your peers, collaborate with the data creator analysis. So it's really enabling very easily that transition to become a data savvy without actually needing too much knowledge of IT or coding, et cetera, et cetera. And I think that is a game changer as well. And enabling that speed that we're all demanding today during these unprecedented times. Gudrun, I wanted to go to you as we talk about administrative evolution, technologies changing. Talk to us a little bit about Oracle Digital. What are you guys doing there? Yeah, thank you. Well, Oracle Digital is a business unit at Oracle EMEA and we focus on emerging countries as well as low-end enterprises in the mid-market in more developed countries. And four years ago, this started with the idea to engage digital with our customers via central hubs 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're already doing. And then about my team, the focus of my team is to have early-stage conversations with our customers on digital transformation and innovation. And we also have a team of industry experts who engage 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 resources to achieve their goals. It's all about outcomes, right, Gudrun? So in terms of automation, what are some of the things Oracle is 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 been innovated year over year, seriously for its launch. And the latest innovation is the autonomous database and the autonomous data warehouse. For our customers, this means a reduction in operational costs by 90% with a multimodal, converged database and machine learning-based automation for full lifecycle management. Our database is self-driving. This means we automate database provisioning, tuning and scaling. The database is self-securing. This means automate data protection and security and it's self-repairing. The automate failure detection, failover and repair. And then the question is, for our customers, what does it mean? It means they can focus on their business instead of maintaining their infrastructure and their operations. That's absolutely critical. Yusuf, I want to go over to you now. Some of the things that we've talked about, just the massive progression in technology, the evolution of that, but we know that whether we're talking about data management or digital transformation, a one-size-fits-all approach doesn't work to address the challenges that the business has, that the IT folks have. As you were 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 IoTahou's seeing throughout the industry? Well, Lisa, I think that 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. Data management, data governments, digital transformation itself. It has to be collaborative and it has to be an element of personalization to data users. In the environment we find ourselves in now, it has to be about enabling self-service as well. 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 to how the data is used and who is using it. And in order to do this, companies, enterprises, organizations really need to know their data. They need to understand what data they hold, where it is and what the sensitivity of it is. They can then, in a more agile way, apply appropriate controls and access so that people themselves and groups within businesses are agile and can 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 Make of Nigeria. You should talk to us about some of the business outcomes that you're seeing other customers make, leveraging automation that they could not do before. I guess one of the key ones is around just, it's automatically being able to classify terabytes of data or even petabytes of data across different sources to find duplicates, which you can then remediate and delete. Now, with the capabilities that Iotaho offers and Oracle offers, you can do things, not just with a five times or a 10 times improvement, but it actually enables you to do projects full-stop that otherwise would fail or you would just not be able to do. I mean, classifying multi-terabyte and multi-petabyte estates 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 and the issues there, as you'd expect, are there is a lot of fragmented data, there's a lot of different sources, there's a lot of different formats, and without these newer technologies to address it with automation and machine learning, the project isn't doable, but now it is. And that could lead to a revolution in some of these businesses and organizations. To enable that revolution, there's gotta be the right cultural mindset. And one of the, when Sanjaga was talking about folks really kind of adapting to that, the thing that I always call that getting comfortably uncomfortable, but that's hard for organizations to do. The technology is here to enable that, but when you're talking with customers, how do you help them build the trust and the confidence that the new technologies and the new approaches can deliver what they need? How do you help drive the tech and the culture? It's a really good question, Ethan, 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 IOTAHO and like Oracle, it's already arrived. What you need to be comfortable doing is experimenting, being agile around it, and trying new ways of doing things if you don't wanna get left behind. And Sanjaga and the team at FBN are a great example of embracing it, testing it on a small scale and then scaling up. At IOTAHO, 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, pick a use case, install the application, analyze the data, drive out some quick wins. So we worked in the last few weeks with a large energy supplier and in about 20 days, we were able to give them an accurate understanding of their critical data elements, help them apply data protection policies, minimize copies of the data and work out what data they needed to delete to reduce their infrastructure spend. So it's about experimenting on that small scale, being agile and then scaling up in a kind of very modern way. Great advice. Sanjaga, I'd like to go back to as we kind of look at again 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 digital challenges and changes that we're all going through? Yeah, that's a really interesting transition, I would say. I was mentioning just coming back to some of the points before to transition with these, I said that the new technologies allowed us to discover the data in a new way, to plug and see very quickly information, to have new models of governing the data, we were talking about adaptive governance and giving autonomy to our different data units. Well, from that autonomy, they can then compose and innovate their own ways. So for me, now we're talking about resilience because in a way, autonomy and flexibility in our 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 any more in the office, you need to give them their autonomy and let them actually engage on their own side and do their own job and trust them in a way and as you give them that, they start innovating and they start having a really interesting idea. So autonomy and flexibility, I think, is a key component of the new infrastructure but even the new reality that pandemic show us that yes, we used to be very kind of a structure, policies, procedures as they're important but now we learn flexibility and adaptability at the same side. Now, when you have that, a key other component of resilience is speed. Of course, people want to access the data and access it fast and decide fast and especially changes are changing so quickly nowadays that you need to be able to interact and iterate with your information to answer your questions quickly. I'm coming back maybe to where you said was saying I completely agree is about experimenting and iterate. You will not get it right the first time, especially that the world is changing too fast and we don't have answers already set for everything. So we need to just go play and have ideas fail, fail fast and then learn and then go for the next. So technologies that allows you to be flexible, iterate and in a very fast agile way continue will allow you to actually be resilient in that way because you are flexible, you adapt, you are agile and you continue answering questions as they come without having everything set in a structure that is too hard. Now coming back to your idea about the culture is changing in employees and in customers, right? Our employees, our customers are more and more digital service and in a way the pandemic has accelerated that. We had many branches of the bank that people used to go to ask for things that they cannot go. You need to, I mean here in Europe with the lockdown you physically cannot be going to the branches and many shops that have been closed. So they had to use our mobile apps and they have to go into the internet banking which is great because that was the acceleration we wanted. Similarly, our employees needed to work remotely so they needed to engage with the digital platform. Now what that means and this is, I think the really strong point of the cultural change for resilience is that more and more we have two types of connectivity that is happening with data. And I call it employees connecting to data, the sales service we're talking about. Employees connecting with each other, the collaboration that user was talking about which is allowing people to share ideas, learn and innovate because the more you have platforms where people can actually find themselves and play with the data they can bring new ideas to their analysis. And then employees actually connecting to algorithms and this is the other part that is really interesting. We also are a partner of Oracle and Oracle in that is great. They have embedded within the transactional system many algorithms that are allowing us to calculate as the transactions happen. What happened there is that when our customers engage with algorithms and again with Ayataho as well the machine learning that is there for speeding the automation of how you find your data allows you to create an alliance with the machine. The machine is there to actually in a way be your best friend to actually have more volume of data calculated faster in a way that it's covered more variety. I mean, we couldn't hope without being connected to these algorithms. And that will finally get to the last connection I was saying is the customers then sell engaging with the connecting with the data. I was saying they are more and more engaging with our app and our website and their digital service. The expectations of the customer has changing. I work in a bank where the industry is completely channeled. You used to have people going to a branch as I was saying, they cannot not only not go there but they are even going from branch to digital to apps to not even wanting to have business services actually in every single app that they are using. So the data becomes a service for them. The data they want to see how they spend their money and the data of their transactions will tell them what is actually their expenditure is going well with their lifestyle. For example, we talk about I don't know I'm a healthy person. I want to see that I'm spending in the good food and the right kind of healthy environment whereas if I'm more environmentally engaged. Now all these is metadata is knowing how to classify your data according to my values my lifestyle is algorithms. So I'm coming back to my three connections is the algorithms that allow me to very quickly analyze that metadata. And actually my staffing the background creating those understanding of the customer journey to give them that service that they expect on a digital channel which is actually allowing them to understand how they are engaging with financial services. And all that engagement is absolutely critical. Santiago thank you for sharing that. I do want to wrap really quickly Gujuan one last question for you. Santiago talked about Oracle. You've talked about it 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 are doing there to help your customers get the resilience that they have to have to be to not just survive but thrive. Yeah. Well Oracle has a cloud offering for infrastructure database, platform service and the complete solutions of it ourselves. And 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. And Oracle has a global coverage with their cloud regions. It's massively investing and innovating and expanding their cloud and by offering cloud 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 maybe one of my takeaways is if I speak with customers I always tell them you better start collecting your data now we enable this partners like IOTao 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 takeaway for today. You can't collect your data backwards. Excellent, good job. Gentlemen, thank you for sharing all of your insights very informative conversation. All right, this is theCUBE the leader in live digital tech coverage.