 Hello, everyone, and welcome to our next DDW session called Managing Data at Speed, Powering Business Growth with Innovative Data Practices. That's going to be presented today by Fiona Fox, Head of Data Governance at Vero, and she's joined today by Afshan Latfi, CEO of the Marikas for Atacama. All audience members are muted during these sessions, so please submit your questions in the Q&A window on the right side of the screen, and our speaker will respond to as many questions as possible at the end of the talk. Please note that there is a linked form at the bottom of the page, titled the EDW Conference Sessions Survey. This is where you can submit session feedback, and we encourage you to do so. It helps us out a lot. Also, there is a small icon to the lower right of the screen, which will enlarge this window with the speaker and slides. Let's begin our presentation now. Thank you and welcome, Fiona and Afshan. Thank you, Eric, and thank you, Fiona, for joining us. Once again, good morning, good afternoon, or good evening, depending on where you're joining us from today. I'm excited to be co-hosting and presenting this session for you. It's a fantastic presentation that we have lined up for you. As Eric mentioned, I'm Joeri Moldy today by a very special guest, Fiona Fox, Head of Data Governance and Enablement at Vero Bank. Fiona is a dynamic financial services leader. She has over 20 years of experience and a proven record of successful leadership in delivering fintech, banking, and for large corporate and financial institutional customers. Fiona has been recognizing for utilizing innovative approaches to data strategy, data governance, data management, AI, machine learning, and advanced analytics to really drive business growth through optimizations of critical data assets. Maybe a quick intro on this session, Fiona, and I'll hand it over to you. Today, Fiona will be examining how data management and governance have often been an afterthought for most organization, which really has led to a more defensive and reactive approach, leaving very little opportunity to focus on innovation and to properly align with the organization's business strategy. So more specifically today, we'll have the opportunity to really view a case study outlining how Vero Bank is managing and optimizing their data today. So with that, I will hand it over to Fiona to get us started. Fiona, the floor is yours. Thank you, Afton, and good morning. Good afternoon, everyone. Thank you for joining today for this case study overview of what has been to date a very exciting journey for Varo. For those of you who may not have heard of us yet, we were founded in 2016 by Colin Walsh who calls himself a reformed banker. And he had, in his mind for a long time, wanted to provide some kind of banking services that really were not just for people who were already doing well, but for many Americans who are and remain underserved and underbanked today. And being very experienced in banking for many years, he had thought maybe he could fix a bank that was already existing to build this bank to service everyone in America. But he realized that the legacy infrastructure and the data that went with it was just too hard to resolve. So he decided to start his own bank. So for the last five years, Varo has been working really hard to create a more enlightened way to provide financial opportunity for everyone. And we've been focusing on tools, products and insights to help our customers become more financially resilient, regardless of their background or their current financial situation. And importantly, we can profitably serve underserved communities, which is something that the traditional banking system has really struggled with. So our journey really started as a FinTech, which means that we were operating as an app on top of a sponsor bank, which had a traditional banking infrastructure. And we went on the journey to gain a full banking license in order to really provide the best of FinTech together with the best of banking in our customer solutions. So why would we stand up a bank with data management and governance? I think everyone here today would probably say, well, why not? It would seem like the ultimate thing to do if you can. We wanted to really start at the beginning and be able to manage our data at the speed of the business as it grows and to power the business. But it wasn't as easy as it might seem. So we had some challenges along the way. If you think of standing up a whole new bank, we had about 30 plus systems, including a new core banking system, a new data lake, and everything upstream and downstream, all going into production at once. So that's a lot of data flowing for a start. And then the second thing was that our data lake is both operational and analytical. Which means there's a lot going on in the single place. Together with that, we operate very lean teams, although we're growing quickly. And we were really taking on three massive programs at once. One was to build a new bank. One was to manage and grow our existing FinTech infrastructure. And the other was to apply for the bank charter, which is a really significant effort on its own. So a lot of work, not so many people, and really very tight timelines, because we had cost constraints as well, being not yet profitable. So we wanted to really use our data to become a truly data-driven company and to be able to scale our data in a way that drives business growth. And as many of us who worked on data governance and management programs before know, we're very often in catch-up mode in organizations that have been around for a while. And I really wanted to avoid that as much as possible at VARO. I've been in large financial institutions and smaller financial institutions over the years and have really seen some challenges with the way we manage and govern our data and the way we implement that. Some of them, which I won't dive into a lot today, because we've heard a lot of great discussions about them earlier in the week, really include the lack of business ownership or being disconnected from business goals and processes. We know that data governance is often seen as an overhead in a defensive way. And often the value is not well understood until regulatory pressure or operational failure occurs, which then leans us not to be able to drive our business with data. In addition, I think we've all seen tools being implemented without processes being around or the wrong tools being implemented or tools that have a long gestation period before any business value is gained. So what I really wanted to do was flip that around and to make the data management really relevant across our product and business process life cycles to be able to show continuous improvement and incremental business value all along the way and to really keep pace of the business and the speed of the organization, which is very agile. So the question of offense versus defense, in my mind, creating a strong offense often enables a very efficient defense. And that was what I really wanted to do here at VARO. So really wanted to ensure that firstly, we're meeting the organization and business where we are. And that resulted in, I guess, some less traditional focus areas that became very important to VARO as we went on our data management and governance journey. So very key obviously was to align with the business strategy, what are we trying to achieve? We heard from John Ladly a great presentation yesterday about data literacy for leadership. And perhaps surprisingly, you might have thought that at a FinTech, the understanding of data or the data literacy and its value might be stronger than in many other traditional institutions. I surprisingly found that some of the same challenges that I've seen before were here as well from a data literacy perspective. So there was a lot of advocacy required to bring people along the journey. And I found that the best way to do that was to jump in and make it very pragmatic, practical to the needs of the organization that was moving fast, as I mentioned. And some of the things that came out of that as being really important to business users from the start was where is the data? How does the data flow around the organization? And how can I access and use the data? You can imagine 500 people asking all of those questions at once when you've stood up, 30 plus systems in production, which is a very unusual situation, definitely. So out of that data discovery, building data flow diagrams and integration and data solutions for new products actually became my first three sort of big focus areas to really support the business. The one important note here as well is that I really didn't want to miss the opportunity to look at this from an offense perspective so that we can really meet the needs of the regulators efficiently going forward and ensure that VARO is very data-driven. So how did I do this? I know not everything works the same for all companies and certainly the key is to find what's best for your company. What worked for me was to build a really robust but maybe lighter than usual policy and standards framework. So I didn't want to end up with a hundred policies that required huge admin and heavy overhead going forward. So that was streamlined into one core data management policy. Then with the standards and operating models and guidelines, user guides being the much more agile and constantly changing sort of underlying support to that policy framework. So it's light, but we haven't missed anything out here, looking at our data privacy, security retention, all aspects of governance. This has really enabled us to build a good framework on which to implement our processes and capabilities. Integrated data management processes and capabilities has been absolutely key. If I told you that we started out with a team of two to support the whole organization, I still quite can't believe it, but that's the size of our team which is now growing significantly, but we've done everything to date with just two people. So in order to do that, I've created some very sort of simple, but broad processes. We often hear about going narrow and deep in data management, picking an area, focusing on it, doing it well and then moving on to the next. I chose the opposite approach here actually which is broad and shallow to enable us to cover all the core aspects of data management. Without any one area falling into disrepair due to lack of resources to pay attention to it. The reason for doing this was my thinking that we could then really cover model and data management processes end to end and then go on a maturity journey as well to deepen that aspect along the flow as we mature as an organization as well. So the only way I was able to do this because I've done this before either with no tools and just in Excel or with fragmented tools that don't talk to each other or with a great tool that takes years to implement but you don't see any business value in the interim. So the way that I've been able to do it here at Barrow has been through this integrated capability where in Atacama I've been able to build business glossary for terms and KPIs, do very broad data discovery by connecting easily to even Excel spreadsheets that might have been on our desktops and variety of our systems and databases and particularly the data lake and then to really profile, get to know the data and to build data quality monitoring around that as well. Together with that, we're also implementing automated data lineage because I've also lived through the pain of manual lineage and retroactive trying to work out the way that tracing data works. So it's great to be able to do this for us right from the start in an automated way that will stay up to date as we grow and change as well. The example I've given here is one of our frameworks of standardized implementation. And I know this is very familiar to everyone but I've really simplified it for us starting with the business process or the data flows. I actually built data flow diagrams for all business areas around the bank. So then it was very easy for us as we looked at a model or some kind of critical business process to identify the critical data, put it into the glossary with its definitions and then profile it, get to know it better, build the data quality rules and then really create that feedback loop for issue remediation as we go around. And this has become a process that's very easily digestible around the bank and has been adopted very easily upfront into our product and model development processes. I mentioned before the shallow and broad approach and one question I've faced a lot in the past has been how do we know when it's governed? Is it done yet? And because of that, I developed this maturity framework for critical data element monitoring so that whatever level you want to put for one, two, three, four, five that this can be used by any organization as appropriate, we could say, okay, today we have reached level one, we're doing our basic data quality monitoring, we've identified our sources and then we can move on from there and the journey for everyone is really clear. So we're still around level one, two today and by the end of the year, we're aiming for level four across the data management framework that you've seen previously here. So what the integrated capabilities have enabled us to do really is to embed our data practices upfront in our product and model development. We've got 300 plus terms in our business glossary to date with their definitions. We have centralized KPIs, that was a bit of a journey. There were a number of disparate sets that we had to bring together. We have profiled all of our critical data around the bank and are well into our journey of monitoring that critical data at that level one and two that I just mentioned. We have 10 models in progress with monitoring this year and we're working on the automation of our lineage for all of those. I must say it's very, very busy for two of us doing all of this, so my team is expanding right now but the fact that we've been able to get here I feel very good about how this sets us up for the future. We couldn't have done this without the integrated capabilities and one area that I'm excited about that we're about to get to is actually the integration of our data lineage in Manta with Atacama. So that we can then actually see, as you can see in the bottom left here, we can then see the quality of each data element as its lineages traced as well. So for me, the value of this is really fantastic and the automated dashboards that I've been able to generate really instantly have enabled me to communicate throughout the organization in a way that I wouldn't have been able to just due to resourcing. So the value has been great and I think our growth will continue and we can scale now as well. I want to leave you with really just some thoughts about where we will be in five years. I do see that data management and governance is a differentiator for companies and I think that's why we're all here today because we all realize this, but I think we'll see more and more companies who aren't focusing on data management will become less relevant and successful as the next five years sort of go. I also think that companies who don't or are not able to adopt agile data management practices will struggle as well because the speed and velocity of our data continues. And I think that we really can use our data through great data management for significant competitive advantage and I'm very excited to continue on this journey. I invite you all to follow our journey as we scale and grow using our data and please feel free to reach out. I'm happy to share any more information on what we've covered today as well. That's great. So Afshan, we have about eight minutes left for questions if you have gotten any in there. Yes, we do. Love to get back on the stage, the virtual stage. There I am, thank you. So thank you Fiona. Thank you so much for the presentation. Extremely insightful, not surprised that we already have quite a few questions. As I go through these questions, maybe I want to welcome the others to take this opportunity to have a little bit more interaction with the presenter today. So feel free to use the Q&A section to write your comments and I'll be more than happy to address them and oppose them to Fiona. So Fiona, a couple of questions I wanted to start and one of them was actually a question that I wanted to ask myself. It starts off with Fiona, very impressive what you've been able to accomplish with just two resources. Now you didn't mention that you were able to go from two to a much larger team. And I think I'll kind of merge my question with this question as well. So just trying to understand if you can share more about your experiences on being able, on how you were able to actually go with that team. So how were you able to show value? Was it an easy conversation or was it a more, I guess you need to show more ROI to the team? What were some of the experiences in growing that team on your side? So I think one of the key concepts as to what it was that everybody has a role to play. So in really getting people on board as business owners and then really saying we're all stewards of our data because we all want great outcomes has been the key to kind of virtually augmenting the team through the business teams. And that has had a great effect of people buying in to understand how it can improve the quality and the output of what they're building. That's been one of the big wins early on, I think. Very good. And I guess maybe to piggyback on that thought. So one thing is to obviously educate the team and talk about the importance of being stewards. But another thing is to really enable them and given the right tools to be able to do so. So one of the questions is what are the technologies and tools that you're using for your business glossary? But maybe if you can even expand and say, what are some of the tools that you use to help enable the team to be part of this sort of organizational approach? Sure. So really we used the business glossary in Atacama. The value for me has been that it's all been in one place that we can enter everything in the business glossary. And then when the business users say, I want to know more about this data that I have here, we can pull it into Atacama, show them and immediately the lights go on and they can see it very clearly and they're super happy. And someone even was able to identify some fraud just from eyeballing the data in Atacama, which was very unexpected, but a great outcome. So I think having something that's ready to go that we could just pull a spreadsheet in from the desktop or point to any data around the organization has been really valuable for us. Wonderful, thank you Fiona. Couple more questions and again, I'll try to kind of put them together as they're within the same context, but there's a couple of questions about the lineage and the automated lineage and how that works. Maybe if you could give a little more insight on some of the advantages of having a lineage functionality that allows you to connect automatically sources and be able to pull things automatically, but then also allowing you to sort of manage yourself within your glossary and take advantage of that information. So if you could speak about the relationship between the glossary, the lineage and what it actually brings to your organization. Absolutely, so I think on the lineage side, that's in my career that has always been the kind of area that just seems the most difficult to tackle, especially when you've got lots of legacy systems and data flows. So here we are able to put code annotations with Manta and also use their scanners to follow our data as it goes upstream into the data lake, into the different zones of the data lake as it's curated and used. And it then creates this map, which is, which can run whenever you like. So you might do it once a week. So it's always going to be up to date, which is huge value. I'm sure many of you have done those data lineage projects that go on forever and a hugely resource intensive. So that's been really, really, really amazing. And then connecting the fields, the technical metadata fields to the business term in the glossary then enables the business user to look at that path of data as it's flowing in the context of the business, which is hugely helpful for them and makes them much more invested as well in whether their operational processes or their analytical processes are working. They're able to see when they're not. So it really does help a lot. Thank you, Fiona. Maybe a follow-up question on that. So you talked about the value of the automated data lineage, so that you don't have to spend months and months to manually gather information. Are there any other use cases you can share where you feel that AI and machine learning functionally has really helped your team to maybe focus more on the innovation and you talked about being proactive in your approach to really give you the time and space to focus on innovation and being proactive instead of just spending this very large amount of time on either gathering lineage information or manually gathering glossary metadata. Oh, absolutely. We've got a really good use case at the moment where we are pointing at a comma as a data source, which is our AIML schema in our sort of AWS ecosystem. So we're now going to layer on the AIML benefits from Atacama onto our AIML schema as well so that we can really leverage and understand the data. And really I think the benefits for that I can't even describe properly because it will really enable us to do so much more, get so many more insights into our data quickly. And Atacama itself is getting to know our data really well through the AI capabilities. Thank you. Thank you, Fiona. There's a couple of questions and I guess you're staying around, starting with a team of two and then being able to grow. It seems like it resonated really well with a lot of the folks attending the session. And there's quite a few questions about how you've structured your organization to be able to manage. So I'll just go through a couple of these and one of them says what was the size and complexity? Apologies for breaking in. We actually have time for about one more question as well. We're just about at time here. Okay, so I will just wrap it up. If you just very quickly in a few seconds, Fiona can give us an idea of what does your operational organization look like in terms of who are you supporting from your stewardship perspective? We're supporting all the business teams. So retail banking, lending, risk, fraud, BSA, AML, the marketing team, brand, finance. We're covering the whole business. So I think I see a lot of the great questions here and I know we're out of time. So I'd like to follow up on all these questions with people who've asked them and provide some more information that might be helpful. Absolutely, absolutely. Thank you. Thank you so much, Alex. So for the other question, feel free to reach out to really Fiona out of camera. More than happy to address them after the session. Thank you. Yes, and thank you, Fiona and Afton, for this great presentation. Lots of audience engagement, clearly a lot of interest. As soon as this video is published, the Q&A will remain live and you will be able to interact and answer questions there as well. Wonderful. Great, so thank you both so much. Thanks to our attendees for tuning in. Please, attendees, complete your conference session survey on the page for this session. And the keynote session with Doug Laney and John Ladley will start in about 15 minutes. So we'll see you there. Thanks so much, everyone. Thank you very much. Thank you, Fiona, have a great day. Thank you all. Thank you.