Data analytics in Banking, Financial Services, and Insurance market size is expected to reach USD 86.68 Billion in 2027 and register a CAGR of 27.7%. The time is now for banks to capitalize on their unique ability to collect data and use insights to build an exceptional personalized experience.
The winners in this space will leverage SMART Data Governance to improve the reliability of data to understand customer behavior, how individual consumers prefer to use products or services, enable personalized offerings, and reflect contextual needs in real time.
Many Financial Institutions have made significant investments into their firmwide data governance programs based on regulatory driven responses, but still view data governance investments as cost centers that have not been able to link to direct value performance and financial indicators.
SMART data governance moves past compliance-focused governance and focuses on the intersection of data governance, data and analytics, customer experience, content, and digital technologies. This panel will discuss how SMART data governance can enable financial institutions to become a data-centric organization that will unlock digital banking value.
Transcription:Jonathan Shiery: (
00:07)
All right, well, let's get kicked off. Um, we only have about 30 minutes and we got a lot of content to get through. Uh, first I wanna just thank, uh, American banker for hosting the event and, uh, all the staff and supporting, uh, and making this happen. It's great to see a lot of people in person. I know 20, 22 has become the, you know, semi return to normal for conferences, so good to see friends and, uh, colleagues over the years. Uh, also honored to be on the panel with, uh, two colleagues, uh, Niloy Sengupta, and Allan Rayson, uh, from Encore Bank. Uh, and, you know, we'll let them introduce themselves in a, in a couple seconds. Uh, just a quick overview of Guidehouse. Um, and an introduction to myself Guidehouse is a, uh, full service management consulting company, uh, and managed services.
Jonathan Shiery: (
00:49)
We have about 13,000 employees. Uh, we have about 300 financial institution clients, and we spend hundreds of thousands of hours, uh, in the intersection of digitization technology, uh, financial services and data management. Uh, and I'm a partner within our banking and capital markets practice. Uh, I've been about 15 years now, uh, supporting financial institutions. I started my career, uh, as a business intelligence developer. So I like to say that I was doing big data before. That was a cool thing to say at happy hours. And over time it's, uh, become more and more, uh, influential in, in kind of our projects. Uh, we've done major transformational projects across M and a regulatory remediations, uh, technology integrations and data management now has created an intersection across all of those and has become a big driver. Uh, so at this moment, let me, uh, hand over to Allan and, uh, Niloy to introduce themselves, and then we'll get started.
Allan Rayson: (
01:43)
Sure. You go first. Uh, it's great to see you. My name's Allan Rayson I'm, uh, with, with Encore Bank, we're based out of, uh, based out of little rock Arkansas. Um, but our innovation hub is here in Austin, which is, which is my responsibility. So I live here. I apologize for the heat. It's not, we didn't, we didn't plan it this way. It's not supposed to be this hot in, in, uh, in June. Um, so we got along summer in front of us, but with respect to Encore, our, our claim to fame right now is we're the fastest growing, uh, fi in the country, our, uh, that, that data got validated by our, um, investment bankers KBW up in New York. So they went and they stripped out all the, all the banks that have grown through M and a, and just kept it to organic growth and were fortunate to say that we're, that we're at the top of that, uh, top of that stack. So my responsibility, um, is running technology and innovation for the, for the company. So today we're about 2.3, 2.4 billion growing, growing rapidly, um, relatively smallish at this point, but, uh, but growing rapidly, uh, we're within nine, we do business in nine states across Southeast, and roughly 21 markets. So if you think about Tampa to Texas, that's kind of the, that's kind of the swath of, of land that we're, that we're in. Um, I'll tell you more as we, uh, as we go along.
Niloy Sengupta: (
03:05)
Thank you. Hello everybody. My name is Niloy Sengupta. I'm a Director and Financial Services at Guidehouse, um, carrier management consultant, about two of experience at the intersection of business and technology, um, relatively new joiner to Guidehousee. Uh, prior to that, I used to be a digital banking principle at one of the larger for professional services firms known for systems integration. Even before that I've worked with, you know, companies like Accenture and Capco, all the top banks have been my clients at one stage or the other in my career, and now been able to consult some of the digital banks and taking them up to, you know, uh, from conception to reality. Um, so happy to be here. It's a topic very close to my heart and looking forward to having a conversation with not just my fellow panelist, but also all of you here, because I think it's a, you know, it's a topic of interest to everybody.
Jonathan Shiery: (
04:03)
Great. Um, so we've gotta pack agenda and like all other 30 minute blocks in our lives these day, we probably have more to cover than we do have time. So we'll try to get it through quick. Uh, we'll start out with why we're even here and why data governance is important for digital banking, uh, go through some of what the smart data governance is, why it's different, uh, and then discuss how it can unlock digital banking value, get a couple key takeaways and then hopefully have a couple minutes for, for Q and a. Uh, so let me hand it over, uh, to my colleagues, Alan, you know, from your experience, uh, when you think about data governance, why is that even important to digital banking?
Allan Rayson: (
04:35)
Yeah, I think, I think for us, um, at, at Encore, I mean, we're, we're managing a bunch of different, you know, a bunch of different lines of business at, at once. Um, you know, kind of our, our thesis behind technology and innovation. We, we skew commercial versus, you know, versus consumer. So that gives us a lot of clarity when, you know, when we think about the, the different businesses that, uh, that we're driving and, and for us, it's about optimizing, um, our commercial lending program. First sec, you know, second is, is driving core deposits. Ultimately, that's how I, that's how I fund my, you know, fund my, my commercial commercial loans, and then, and then third non-interest revenue. Um, and the reason that, like that type of clarity having that type of clarity is, is important because it, it then informs, you know, virtually everything, everything we do from there. So, um, we'll talk, we'll talk more about this, but, you know, starting from a place of, um, you know, a lot of clarity around the outcomes that you're, that you're trying to produce and then sort of building governance, data, you know, data governance and, and otherwise, uh, in behind that, but ultimately it drives, uh, the business outcomes that, you know, that we're trying to realize kind of day in and day out inside of a fast growing, a fast growing
Jonathan Shiery: (
05:57)
FFI. Right. I know you have a lot of experience in this space of trying to pre create that connection between technology digital banking and, and the business side. So what have you seen as the importance
Niloy Sengupta: (
06:08)
Of, yeah, so, so, you know, one of the interesting things is some of the primary business drivers for digital banking is also the same as data governance. And if you look at that, you know, uh, we, I think we all understand that data governance, the primary business driver is, uh, regulatory and compliance, but I'd like to challenge the thinking and say that if you look at digital banking, the drivers for which are, you know, uh, increasing customer experience enhancing the customer experience, and then faster time to market, uh, products and solutions are even, you know, uh, managing TCO data governance is actually key to all of these things, right? And I'll give you a small example, just, you know, as a notion. So one of the digital banks, it's actually a digital arm of a brick and mortar bank, and they were trying to set up this new digital bank.
Niloy Sengupta: (
07:03)
And, uh, they bought all the nice tools that you can get, you know, uh, and one of the things that they were trying to do is, um, to have this application onboarding faster, uh, so they thought that they'll prepopulate all the data that they get from the prospecting stage, right into this application onboarding process. So that the customer, the new customer doesn't need to enter anything will just look at all the fields and just will edit if necessary and then move on. That will bring the experience that will greatly enhance the experience and, you know, make it very fast to open an application. So when they did the pilot implementation, unfortunately it did not work out that way because all that data that they had in Salesforce for prospecting, they were not governed well. So when they did the pilot, the pilot customer still had to edit all those data. Right. So that shows you the importance of having a good, robust data governance program, not just for the reasons of compliance and monitoring, but overall to, for other business reasons as we are talking about. Yep.
Jonathan Shiery: (
08:10)
Yeah. That makes a lot of sense. I think, I think what I I'm taking away from that is, you know, you've got kind of three different dimensions. When you think about digital banking, you've got the user experience, you've got tools and the, the workflow, the technology, and then you got data flowing through that, right. And so, you know, if you do it right, you should be aligned to the strategic corporate objectives, which will give the outcomes that you're looking for in commercial banking. Um, but if you don't do it right, then you can end up in a situation where you don't unlock value, actually decimate value through, you know, whether it's biased data incorrect or inaccurate recommendations. And so data governance is like the enabler of the, that data to go through and create that value. Um, hyperpersonalization faster efficiencies, things like that. That's right.
Jonathan Shiery: (
08:51)
So as we go through, then, you know, one of the things that we've kicked around and, and many of you may have, uh, and I assume just because of the attendees, uh, and, and the track, uh, have experienced this along the way, but there's been a lot of, you know, quote unquote boiling of the ocean when it comes to data governance. There's been a lot of friction between data governance and the businesses, uh, on, you know, are you adding value? How are you adding value, uh, or are you just creating additional lines of red tape for us to, to manage and, and actually, uh, produce for our clients and customers? So we've been talking a lot of times around smart data governance and what smart is. And a lot of you may have heard of the smart along the way, you know, the specific, measurable, actionable, relevant, and then time based.
Jonathan Shiery: (
09:32)
And what we did is we took a little bit of a different angle to that, as you can see here and read these, um, from a data governance perspective, but at the end, the end of the day, it's about having a, a program and a data governance solution that's aligned to your strategic objectives and growth objectives. So you have to be, you have to be scalable, but then you also need to have clear metrics to measure that. And it's more than metrics past the traditional compliance metrics or, uh, the governance metrics it's, it's around, how are you adding value and, and what are the outcomes that you're, you're bringing through, and you need to be able to evolve with the business as it changes, uh, focused on the highest risk items. And then, you know, traction is an important one. Uh, it tends to be intuitive in some ways you think so, but, uh, you need to find allies within the business, uh, be very focused and, and specific with your data governance tools. And then, you know, you build that traction with those allies and, you know, through solid business cases and outcomes, uh, and then you create that culture, culture of data governance. Uh, so, you know, as we thought through that, you know, we start to think about, you know, what's the difference between smart data governance and, and traditional data governance, and so way. Let me ask you, you know, from your experience, you know, what have you seen in traditional data governance? Um, that would be different and how we're, we're thinking about smart data governance.
Niloy Sengupta: (
10:46)
Yeah. That, thank you for that question. And I think as I think through it, um, you know, traditional data governance focuses too much on data quality and maybe just, you know, the operating model around data governance, where data governance, um, is an accountability of the chief data officers, uh, you know, responsibilities. But I think it needs to be more than that. Uh, if you look at obviously data quality is important, but one needs to look at data, life cycle in its entirety and things like, you know, how do you get to that single version of truth? They're just so many applications in an enterprise. And if they have multiple, you know, versions of the same data, then an organization quickly needs to focus on finding out those gold copies of data. That's very important. Also rationalizing the data sources, uh, especially if you are keeping all of your data in an enterprise data league or something like that.
Niloy Sengupta: (
11:41)
How do you make sure that you are getting, you are managing those conflicting data sources, right. And keeping the right versions of data, always up to date, and then, you know, um, data, data governance needs to be more data at rest and motion. And I think you alluded to one of these that there needs to be a, a consistent operating model evolving where data governance is a culture in itself and not just a program, which has a start finite end date, sorry, start date and end date. Right. So it, it has to be much more than that. Mm-hmm
Jonathan Shiery: (
12:17)
and Alan, I've seen, I'm sure you've seen both ways where good data's helped you and bad data's helped you. And also data governance has gotten your way of getting things done. So what, what's your experience, uh, and why does smart, you know, would be important for traditional data Allan Rayson: (
12:29) Governance? No, I mean, I, I think you commented on something, something that's, uh, super important for, for us in inside of Encore and many other organizations, but that's, you know, that's the word culture, um, you know, ultimately ultimately inside of a inside of an fi you're trying to inside of a bank, you're trying to drive certain, certain outcomes. You're trying to grow, you know, trying to grow lines of business, whether it be commercial or, or consumer, but, you know, for us, uh, I think the culture around data that we have and, and governance that we've built, uh, is really important to us from the standpoint that the other, you know, the other side of the, of the equation is the, is the user experience for our, for our clients. And, you know, one of the things we were sort of brainstorming the other day is the topic of, of digital account opening products and, and digital account opening workflows that we take, uh, prob we, the industry take, you know, many people through these these days, you know, ultimately we're trying to, trying to open accounts in a very, in a very efficient way, uh, hopefully in, in roughly two and a half minutes or less, but where that gets off track, you know, ultimately comes down to the data that we're trying to collect. Allan Rayson: (
13:47) You know, if we're, if we're taking a, if we're taking a user through, you know, through a workflow, that's supposed to be about two and a half minutes and it takes 'em nine because we've, you know, because we've tried to collect and manage, um, you know, 12 different data points that aren't mission, critical mission critical to the, you know, to the outcome. We ha you know, what have we have, we really accomplished if we're, you know, missing the mark by five X in that, in that workflow. So I think ultimately that comes back to, you know, a framework like this, that, that finds that balance, um, that we're looking for Jonathan Shiery: (
14:24) You, you know, and you, you mentioned that when we were doing our prep session, spending the weekend, thinking about it, and, and it's interesting, cause I think that's a great example between traditional and, and smart data governance and the aspect that a lot of data governance programs have gotten, you know, essentially bad rep for Alaska, better, better terminology, because they've tried to collect everything or they've tried to boil the ocean and they've treated all data the same, right. Um, that's right where, you know, what you just said is, you know, the smart way is you need to get the right data. You need to make sure that it's secure, it's compliant and stuff, but you need to also balance the, the value that you're, you're going along the way. And, and I think that's, you know, in your policies, in your data governance frameworks, you know, you can create that flexibility, right? Jonathan Shiery: (
15:06) Like you, you know, and that's the key is to make sure you're maintaining the flexibility, um, that, that increases the value to the business and then demonstrates that through a measure. Um, and, and I think that's just the key difference is like taking, you know, your traditional data governance frameworks, um, where there might be a one size fits all, or there might just be focused compliance metrics, um, or there might be, you know, a lack of flexibility, um, and bringing that, you know, flexibility and practical, you know, tie into the, the business outcomes and, and doing that gives you, gives you a greater, um, you know, integration with, with your business strategically and, and the overall, uh, impact you can make with data. And then we'll win fans and you won't be explaining, you know, the value of your business cases over time. So I, I think that's a, that's a key point and, you know, thanks for that example. Jonathan Shiery: (
15:50) Cause I think that's important difference between what we've seen in traditional versus smart, uh, I guess as we go on, you know, when you think about the, the key takeaways and, and you know, the thoughts that we, we want, uh, to, to, for you all to leave with, you know, one of the ones that we think, uh, and we spend some time talking when we were prepped is, you know, what, how are you aligning to your corporate objectives? And, and we've seen a lot of programs, and this has become a, a, a very focal point for banks is taking your data governance and saying, okay, I'm doing this because this is the impact that it has to business. If you can't say that because of how it's impacting the business with any of your data governance requirements. I think that that is a gap that needs to be filled, you know, first and foremost. But when we think about, you know, overall like how data governance and smart data governance and these takeaways can impact digital banking, you know, Alan tell me, um, you know, where do you see like data governance providing the most value for you in like your digital banking outcomes? Allan Rayson: (
16:47) Yeah, I mean, just, just that first one for me, the, the corporate objectives have got to be understood, um, you know, within the organization from, from the beginning in my, in my opinion, no matter, no matter where someone is inside the organization, whether it's, you know, on the sales side of the organization or the technology or the data, or, you know, where wherever it may be, sorry, um, you know, understanding and having that dialogue with, um, you know, with the CEO or with the C-suite of the organization to understand, you know, what, what is it, what, what objectives are on are on your radar, CEO and chairman, or, um, you know, growth officer, whomever. It may be if, if I can understand, if I can understand what's on their radar and the outcomes that they're trying to produce, that helps me back into virtually everything, cuz I now have context on, um, you know, what's, what's ultimately on their radar and what's on the, you know, what's been committed to at the, at the board level, but you know, I think in my opinion, it, it does start with, um, you know, sort of a mixture of, of one and four, maybe getting, getting out of those, you know, getting out my silo, understanding the, the bigger picture, um, and kind of aligning everything around that. Allan Rayson: (
18:13) Maybe what, what's your thoughts on Niloy Sengupta: (
18:16) How yeah, and I actually, you know, I want to use that opportunity to ask a follow up question to a, if I may and, um, you know, Alan, you know, when you drive an innovation organization and one of the common perceptions of any governance program is governance and innovation do not go well together because you know, governance kind of impedes on the piece of innovation. So when you talk about data governance, obviously there's a cost of quality and there is this perception to battle that it might some way, you know, impede on the piece of digital transformation. How do you manage that? Mm-hmm Allan Rayson: (
18:55) uh, depends on the day. Um, you know, I, I think for us and, and admittedly I'm, I'm inside of an organization with 287 associates, including me, so relatively small, all, all things considered. So that's, that's the first thing I would say, cuz it's important to have important to have that context, but I think for us to your question virtually, virtually everything we do starts with a core team of, of people across a lot of different disciplines inside the organization. So, um, you know, we've, we early on, I've been with Encore a couple of a couple of years now. Um, but early on we formed and, and put together these, um, you know, lack of a better phrase kind of multidiscipline teams across, uh, risk audit, compliance, ops tech innovation, and, and brought the right people into these, into these teams that are kind of leading these, leading these areas. Allan Rayson: (
20:02) And that's where we start with everything. I'm not out, I'm not out doing my own thing while, you know, Carla on the, on the op side is, is doing hers or Erin or somebody like that. We are coming together on a weekly basis. Um, you know, oftentimes multiple times a week and that's where virtually every project starts. So it's not all perfect. I don't want to create that illusion, but starting with that core, um, you know, kind of multidisciplinary team is works really well for us. Uh, because then, then we can kind of provide each other with, with different context on those objectives that we're trying to create. But you know, if I'm running audit, I'm gonna have a different agenda than, than if I'm running risk and you know, same difference with compliance and ops and tech. Everybody's gonna have a bit of a different lens and a bit of a different agenda, but it's worked for us, uh, ultimately, um, and I think there's probably some takeaways there. Hope Niloy Sengupta: (
21:07) That's a great thing. Uh, so Jonathan, you know, in your role as a client partner to multiple large organizations, you might have faced similar, uh, you know, challenges driving where your clients have this issue of balancing governance and innovation needs, what have you seen your clients and do? Jonathan Shiery: (
21:28) Yeah, so I think, I think when you think about governance, um, you know, a lot of people have different definitions of what that means and different perspectives good and bad of that. And, and at the end of the day, governance is really set up to create and organize like, and, and create a transparency in your decision, making, set standardized processes and, and enable consistency that everybody's working on the same page and can speak the same language, right? Like that's at the end of the day, what you should get out of governance, um, as a tool and a solution. Um, so you know, what I always look for is like the data journey mapping there, right? Like, you know, there's been a lot of focus over the years on customer journey mapping. Well, within your digital processes, what's the data journey map that you're having there. Jonathan Shiery: (
22:11) And then, you know, what is the governance as a tool and as a solution, if you do it smart and you apply it, like how can that either reduce the risk, which is one of the biggest factors that data governance can do, cuz you've got standardized data, um, you've got secure and compliant, you know, mitigate your litigation areas or your regulatory compliance areas. Uh, but then how does it enable it? Like when you think about like you're an AI solution, like how, how can we use data governance to make sure we're sourcing the data correctly so that we don't create inherent bias into our data, right? Like that's, that's a smart problem. It's a real issue. And if you can come up with a solution, your data governance program solves that for somebody there's value there. Um, so, so I, I think it's really important to understand the data you need. Jonathan Shiery: (
22:51) I think what you talked about, what your mission critical data, how that's hitting each part of your, your, your journey there. And then how does governance create the solution? Like you don't wanna start with governance, right? Like you should start governance should be part of the solution, not, you know, not, not the solution. Right. So that's what I've seen be more effective over time. I think it started out with like, we just need data governance and we need to, you know, all data's the same, I've spent months in rooms with large organizations talking about the definition of what data is, um, is that, you know, should you govern data that's on somebody's laptop in an Excel sheet like, well, I guess that depends on what they're using that data for. I don't know. Um, but there's a lot of people that look at data very differently. Jonathan Shiery: (
23:27) So, you know, you can, you can boil the ocean and, you know, remember the days when you started talking about business continuity and you know, like what, what if an asteroid hits the earth? Well, yeah, that could probably happen, but you know, we have bigger problems. Should we really spend our time, you know, planning around that or what's more plausible and practical. Um, and I think that's where, you know, smart data governance is trying to push, uh, the concept is like, let's get practical, let's use data governance as a solution and as a tool versus, um, just the, at the end all be all for, you know, every application's the same. Allan Rayson: (
23:55) Yeah, that's good. So Jonathan Shiery: (
23:56) I think we're, so we have about five minutes left. Um, you know, some of the other key takeaways, you know, I think you've heard us talk about this multiple times is, you know, making sure that you have the right measurements in place, uh, making sure you have an outcome based solution, can't reiterate that enough. Um, your, your solution, your data governance needs to solve, you know, business problems. Uh, and then making sure it's scalable with modern tools and techniques are some really slick tools out there that are even using AI, um, and others to, to create a data quality, uh, environment that, you know, enhances your, your, uh, governance and structure and your compliance. So there's a lot of, you know, innovation tools coming out and, and change management, you know, everybody's bank, you know, one thing's for sure you're growing and changing. I don't, I haven't talked to one executive at a bank that can't says like, things are just boring and every day is the same. Like everything's changing so fast so that you need to be able to have a program that can change with your priorities. Uh, anything else, like, you know, Alan before we move it over to Q and a, that you think would be important to highlight or any, any final kind of leading part thoughtful thoughts? Allan Rayson: (
24:59) I think, I think you commented on, on something. Um, and maybe you'll trademark it for, for all of us, but you know, inside of a bank, not every, not every piece of data has the same, you know, has the same weight. Um, and, and admittedly, we are not all the way there, but you know, trying to get to a place where we've got all these, you know, all these data points across consumer and commercial and mortgage and SBA, we've got hundreds of different data points, but you know, all of those don't necessarily, and, and they don't have to carry the carry the same waiting. So, you know, I think one of, one of the things that I'm trying to do is, is get us to a place where we have some context around, you know, maybe it's maybe it's categorizing three different, you know, three different waitings that, that help us all understand, okay. You know, category one is, is mission critical. We gotta, you know, we've gotta capture it, we've gotta govern it. You know, maybe maybe level two and level three are, are, are less, so maybe less important, you know, to the overall outcome. But I think it's a really important concept to, you know, kind of explore within our respective organizations as is, you know, what is the waiting that we're, that we're assigning to, you know, different pools, I guess. Niloy Sengupta: (
26:20) Yeah. And one of the other things I can think of, and we've talked about it multiple times here is not look at data governance as a run the bank initiative, but as an enabler for change the bank initiatives as well. And to that effect, if we can integrate data governance as one of the, you know, necessary objectives or outcomes of any change initiatives, whether it's digital transformation or operating model changes, I think that leads to a greater possibility of success for data governance than a siloed data governance program itself. Right. So, so that's, I think would be a key takeaway from me as well. Yeah, Jonathan Shiery: (
27:01) That's good. Yeah. And I, I guess the last thing I would say is that it all leads with your policy and, and those standards that you might have at your firm. Like if you have a data governance policy that's not flexible, then you're gonna continue to bounce your head up against the wall and be frustrated by the friction it causes with the business. But if you create the right levers and within your policy and you strategically put your standards in place, so the business can do what they need to do, but you, you Mo focus on the, the high risk areas. It'ss scalable. Like, I, I think you'll get the right outcomes or you get better outcomes as, as, as you continue to evolve. So I guess we have a couple minutes, uh, Alan, I know you're, you're in demand. So you got about three minutes till you have to go to your next speaking session, but, uh, is there any questions or anybody wants to ask? Yeah, sure. Audience Member 1: (
27:41) Yeah. So quick question. How do you balance the, um, the data that you need for say risk management, the high quality confident can't miss, you know, for your financial and that versus the speed that maybe the consumer, you know, or the marketing department would like to get with the data in two disparate cultures, but obviously coming off of similar, you know, data, which is, you know, your, Allan Rayson: (
28:11) Yeah. Ultimately, I mean one person's opinion, but, um, ultimately it does come down to the culture for us. I mean, we, we work hard, you know, on the front end of a, on the front end of a project to agree on, you know, that those waitings or those hierarchies, so that once we're, you know, once we're in the weeds together, there's less conversation. Um, and we've, we've kind of already agreed on a framework, uh, that we're gonna, you know, that we're gonna prioritize off of. Niloy Sengupta: (
28:42) I I'd just like to say, you know, uh, when thank you for that question. That's a great question. So when you think of governance, uh, let's not think of it as a monolith in terms of like the controls and the matrix that are needed for compliance may not be the same when you are using it for other business purposes. So the standard deviation or the tolerance of variance for quality measures could also be different when you're looking at marketing data versus a data needed for compliance perspective, right? Jonathan Shiery: (
29:18) Yeah. I think those are two different outcomes with a set of risks. Right. And so, again, that's why, like, I think data governance programs need to align more to the outcome based focus and, and less around like, just creating it because we want to make our regulators happy and hand them over our standards and policies. Right. Like, and the more we connect to that and make that policy evolving or, or I, and gotta be careful with evolving, but flexible. Um, you know, I think you'll get a better outcome. Thanks for the question though. Allan Rayson: (
29:49) Yeah. The lady at the back. Audience Member 2: (
29:51) The gentleman from Encore, you said we put together multidisciplinary teams, what type of roles, teams, Allan Rayson: (
29:58) Uh, primary roles, uh, were, are a risk audit, compliance, ops tech and innovation for us that, that captures, um, you know, minus the sales aspect of, of running the bank that captures our core disciplines across, across the organization. We gotta get you. Thank y'all so much. Yeah, this was great. Jonathan Shiery: (
30:27) Thank you everybody really.
