 So I can't believe it again, I gotta see this. How many people are traveling for the first time? Geez, that's a lot of people. So I'm really excited that you guys decided to come to the DGIQ conference. This is probably my favorite conference out of everything that I get a chance to attend. I think this is my third or fourth year in a row outside of the one that came up last year. But one of the big things that I always have, there's two elephants in the room that I wanna address. One, I always like to leave somebody at the conference with something. So hopefully you'll have some takeaways from this particular keynote. I run this probably differently than any other keynote before. It's not a whole like, oh, this is all sunshine and roses. I actually wanna give you some sort of meat to go back to each of your respective organizations and take and deliver on something. And then the second elephant in the room is my hair. Cause I get this quite a bit. There is a gel called Johnny Bees. It's purple. You can get it on Amazon for about 17 bucks, but it's really cheap hair gel. In order to get my hair to look like this, I get the comment more than I care to admit. So normally this would be a slide that Jim Tio speaks to from the Enterprise Data Office, Vision and Mission, but Invesco's vision for data and content, realize data as a strategic asset, providing quality data and content to the right people at the right time in the right format, giving us global, regional and functional insight. And honestly, the whole way you can do this is through operationalizing something called an Enterprise Data Office. And so we are dedicated to empowering the business of Invesco by delivering trusted solutions through complete data and content enablement services delivered globally to be consumed locally. And I'll talk about what we mean a little bit more like that, but we wanna create efficiency, enable governance and I'm gonna highlight that word right there because that's near and dear to my heart and continuously execute and innovate the data strategy. From a created efficiency perspective, we wanna build these things out of best practices and so you'll see in the data governance space right here, industry best practices. We wanna take those best practices that we have, build them as capabilities and services that each different domain office can have and then bring those back into the locally. And now when I mean locally for Invesco, that would mean something like distribution, which is client product investments, which is obviously investments and then corporate services, which would be more of your traditional HR and finance. So build them at the enterprise data governance level, enterprise level I should say, and have them consumed at those domain levels. But we wanna do it to make sure that we're doing it from a value add perspective. The people in those domains are really good at their jobs. They're not very good at things like data governance because they're not supposed to be very good at those things. They're supposed to be good at things like finance, corporate services, HR, distributions and investments. We are the professionals when it comes to data governance, data management, BI analytics and things of that nature. So what we wanna do is we wanna make sure that they can focus their time on those value add things and then let us, the professionals, as it comes to the data stuff. We wanna reduce the overall in content expense by eliminating low value duplication and complexity. That's continuous improvement that Tony had mentioned. I have a passion around. We have to make sure that we're eliminating those duplication efforts as it relates to maybe data management, data governance practices and principles and things of that nature. We wanna improve the speed to market by placing the consumption as close to the data as possible. So when we say as close to the data as possible is when I have errors in my data, go back as close to the sources I can to fix them because as we all know, show of hands, as it starts to replicate through, what happens? It gets much, much more amplified. You do that, I'm gonna get my water right over here because I already have cottonmouth like you wouldn't believe. Thanks Tony. We wanna deliver that through and the maintenance and enforcement of a sound governance strategy and content management policy and standards across the organization. We wanna reduce the risk and return by providing consistent oversight and governance and I'm gonna talk about the governance more here in a minute. And then we wanna drive forward all the dimensions of an ecosystem across Invesco and we're gonna talk about what that actually means here in a little bit. But how do we do this? What are our guiding principles as it relates to an enterprise data office for Invesco? We are one team, we are one team that includes those domain data offices as well. We are one team comprised of data first people, processes, technologies and systems, not a collection of individuals. That's a huge thing, I'm gonna repeat that again. Not a collection of individuals. We wanna seamlessly collaborate to drive the true value of data. If you do not collaborate, you will not see the fullest value of your data. Just straight up. We are dedicated to empowering the business. The enterprise data office I'm gonna call it and this is sometimes like a taboo word. We're a shared service, right? Let's call it what it is. We are a shared service. Sometimes people in finance call us like a pit of money but that is what it is. We are a shared service but we are delivering complete and trusted data solutions to our business partners so they can concentrate on driving what the value is for the business. And we wanna have a best in class data office that includes those domain data offices together to bring a holistic life cycle together for speed, agility and productivity. So what do we look like? This is actually what we look like and I'm gonna take a quick drink right here while you guys look at all the copious words on the page. So there's five functional areas in our data office. Data governance is obviously the one that I lead so we'll talk about that one last. The first one is data architecture. So they're responsible for definition and maintenance from an architectural perspective, framework, design, application perspective. They liaise with the enterprise architecture from technology so we do not live in technology. We are actually standalone function so we are peer groups as it relates to our IT or technology function. Data security and privacy, they also architect that in conjunction with data security and privacy as well. From a data systems perspective, me and the leader of this organization from a data systems perspective are the ones that are basically peas in a pod. We are, he is the right hand to me being the left hand. I cannot do data governance without these particular areas. Think of them as kind of the drivers of the data management technology and tool stack. These are the individuals in data systems, right? They also liaise with technology to make sure that the systems that we use to govern our data are actually up and functioning. They actually work with our data quality technology, metadata technology, data lineage technology. These are the individuals that do this from a data systems perspective. Information delivery, think of that as if data governance and the data advisory services being the front door, information delivery is the back door. They are the ones that are out in front of the business saying, hey, this is the best practice you can have from a virtualization perspective, a visualization perspective, Power BI, Tableau, whatever it might be. These are the individuals working directly with the business that have some sort of function out there as it relates to data reporting, data analytics, data science, information delivery. That is why they are called information delivery. Data advisory services, these are the ones that partner directly with those domain data offices that I talked about. This is what this particular group does. They are the front door, they are the ones that talk to the business pretty much every single day. And those matrices, relationships that you see right over here on the screen, those domain data offices specifically are the ones that DAZ data advisory services liaise with all the time. They are the ones that talk to technology all the time, the ones that talk to the privacy and security all the time. These are the ones that bring basically the business in the door as it relates to an enterprise data office. Everything that we do is typically done through a relationship with DAZ. And they are one of our peer groups too. But all that's cool and all, but I'm actually here because of that. Data governance, we are providing taxonomy, cataloging, master data management, meta data management, data quality assurance and remediation, end user training. That's another big one. That's probably one of the biggest takeaways you can have here is you cannot under emphasize this whole concept of governance training and communication. You have to talk about it, you have to train them, build that fully functional first line of defense by being a sound good partner from a second line of defense perspective. Help them build it together. That way when you hand them the keys, they don't crash the car. That's effectively what we're trying to get out here. So I talked about data governance, I'm gonna talk more about it. Business value and risk reduction. We are the engine that drives all of this. Data governance and data management, I believe, is one of the key functions as it relates to any organization. We decrease data risk, strengthen risk posture and increase the quality and understanding of data. This is one of those meaty slides that if you guys wanna take and download and then, well, don't blatantly rip it off, but you can take the words off the screen. This would be one of those good ones. I got a couple other good ones in there too because there's one that talks about what is data governance and what is the data governance. I got that one too. We wanna know where your sensitive data is located. I wanna know where all of your data is located. I can't do quality, I can't do lineage, I can't do any of that unless I know where your data is. So I wanna know where your data is and I wanna know where your sensitive data is because then I could put the right controls in place rather than blanketly saying, I wanna fort knocks all of my data. That's really expensive. You shouldn't have to do that. Let's look at the critical data assets first, find out which ones have the PI, PHI, PII, PCI and then put the correct controls in place as needed. Wanna make sure that we're testing those controls. Do we have the right controls in place as it exists or are they a little, I could probably have a little bit better controls. We wanna improve compliance to the retention schedule. Now, I can already see somebody out there that's like, well, you're gonna give it all my good analytical data, what about that? I'm not saying purge all of the, well, I kinda am saying purge all that data, I guess I should say. Don't keep it longer than you have to but if you have some sort of argument out there and somebody goes, well, yeah, but I need it because of analytics, you can say, all right, fine, let me strip out the PII, the PHI and the PCI because every data scientist that I've ever talked to has said, I don't need to see a social, they don't need to see a social, let's get rid of it, let's purge it, let's purge that data and you can keep all the rest of that data until the cows come home, I don't care as long as we have reduced the risk of that data by following a retention schedule. We wanna manage access to the sensitive data, I wanna manage access to the majority of the data and this is a fairly unpopular opinion, I actually wanna give more access to data through this concept called data democratization, there's probably gonna be a couple sessions and that word is very fancy right now, data democratization, I wanna give access to all of the data that I possibly can through sound governance principles and it's totally possible. We wanna strengthen our risk posture through identifying where are we mature? We happen to use CMMI's DMM from a maturity perspective, that's what we use to basically check ourselves to see where we are from a maturity perspective and then let's find out where our gaps are and then let's put the right controls in place to close those gaps. We wanna make sure that we have best practices in place as it relates to processes and procedures. We want to be known as a second line of defense from our internal audit partners. That's another thing that we work on, every organization that I've had a chance to be a part of, I've had the luxury of being bequeathed a second line of defense because I actually work towards becoming a second line of defense. When a prioritize known risk for resolution, I'm gonna talk about more about this in a little bit, we have this concept called data quality, integrity and sufficiency, DQIS, that's actually one of our oversight groups, that's our quote unquote second line of defense that we have in our organization. We wanna do that through strengthening the relationships with privacy, security, cyber and audits. I want to be best friends with internal audits, not like oh god, internal audits is coming, don't look, don't look, I wanna be best friends with internal audits. Great story from a risk perspective, but what about the increasing the quality and understanding of that data? I wanna set data quality rules and thresholds in place by going and talking to the business, hey, what's good for you? What does good mean to you? I wanna have those conversations directly with the business. I wanna define the data for them, business rules, metadata requirements, data lineage. Remember I talked about that relationship with DAS? Guess who also has a relationship with the business? We do in the data governance office as well. We wanna cleanse those data issues close back to sources we possibly can, utilizing existing tools that we have in place. Remember we want to be the provider of choice as it relates to the shared service that we are. They could go out and hire any consultant that they want, but we wanna be the shared service provider of choice from an enterprise data office perspective. So simply stated at the bottom, enhance the data experience, enable the future, reduce our risk and maintain the lifeblood of Invesco, the data. This is one of those other slides that is really, really popular. What's the data governance? I don't know what that is. So I had to create this slide, not just for this conference, but honestly, because I needed to make sure that we were grounding ourselves into what are we and what are we not? So data governance is not an initiative with an end date. It is not an initiative with an end date. I'll scream it louder for everybody in the back if I have to. It's a sustainable ongoing program with data oversight, framework, operational model, policy, standards, procedures, best practices, all in support of the data. It is not have a start and an end date. It's not done in a silo either. It's not a single team. It's not business or technology. It's business and technology in collaboration with one another. Enterprise-wide effort requires both technology, IT, and business together. And it's not a single set of tasks either. It's a robust framework, metadata, data quality, reference data, user access, data retention, data lineage. That's not something that you just do one off. That is a sustainable ongoing program. This one, I got this one a lot. I'm not a data owner. I'm a second line of defense. I am a shared service. I'm an oversight partner with the data domains. I enable them through robust practices. That's what I do. I don't own their data. They own their data. They're accountable for their data. They're responsible for their data. I give them services that they can render in order to make their data and their data programs better. And it's not a nice to have. Not having a well-managed data governance organization or framework can actually be an impediment to becoming a data and digital-driven organization. What you do when you need trusted, available, usable, integrated, and secure data, you have a robust data governance program. That's what you do. That's what you need. That's why I believe data governance and data management is an engine that drives a data office. So what is the value that we provide? This is another, probably one of those, simply stated, we want the right people to have enterprise data that's trusted, understood, accurate, and can be used in a meaningful, consistent manner. We wanna be the provider of choice for the business domains. We wanna make sure that we operationalize these governance activities and these management activities. That's what we do. And we ensure high-quality, consistent data for our business partner's consumption. We do that with and for them if they do not have a fully functional first line. That's what we do. We wanna help with privacy, compliance with regulatory requirements. Every new regulatory requirement that feels like comes out every other Tuesday. We wanna make sure that we are in line with those data regulations as well. And data quality that meets the business and regulatory requirements. What's the business requirements? Not what I think it should be. I think data should be 100% accurate all the time. Is that okay? No, it's really, really expensive for you to do that. So you don't have to do that. So let's make sure that we're putting the right quality of data in the right spot at the right time. Now, I really love this slide and I'm gonna talk about what it makes, the top, I want you to think of a grocery store. So everybody get your favorite grocery store in your mind. Could be Ralph's, could be Kroger, could be Giant Eagle, could be Publix. What else do they have out here? Trader Joe's, Albert's. Okay, so you guys got, okay, you got it. Think grocery store in this very next slide. Go with me on this, this will make sense I think. If not, I apologize for any confusion. Product to consumer, store receives a product. Maybe it's your favorite box of Lucky Charms, okay? You love Lucky Charms. It's the magically delicious thing that gets you every time. They come to the store. That product is inventoryed. They scan it in and that product is put on the stock on the shelves specifically. But what if that product was in a brown bland box and they went and put it on the shelf in a spot that was not labeled at all and it's just brown boxes after brown boxes after brown boxes. None of it is catalogued. None of it is inventoryed. You have no idea where the label is. It's just a brown box. That's a terrible experience. You have no idea if you're getting a box of Raisin brand or you're getting a box of those Lucky Charms that you need. So a store catalogs their data and they put that vital metadata on the shelf for you to find it. But one of the other things that they do, does it have the correct labeling? Is it still good? Is it actually out of date? Is it stale? They're checking from a quality control perspective. Then what happens? Then you come into the store. I'm looking for my favorite box of Lucky Charms. I need to know where that's at. So from an engagement, sharing, customer service, search optimization perspective, I know that if I go to aisle 14 on the top shelf on the right halfway down the Lucky Charms are gonna be there, right? Why do I know that? Because maybe I pulled up the app. Maybe the labeling on the sign of the store says serial on it. And I know that I'm in the general vicinity and I know that General Mills is in this particular section and Post is in this particular section. That's all metadata. That's nothing but metadata. Data about the data. The data piece just happens to be Lucky Charms. And then I want them to take some sort of action. I want them to buy it, right? I want them to buy it and then I want them to scream from the hill tops. This is magically delicious. This is the best thing ever. That's what I want them to do. Accessibility, trustworthiness, insight, socialization, decision making and follow through. That's the life cycle of what I want them to do from a serial perspective. Now let's think about this from a data to consumer perspective. Acquisition of data. Data comes in. I don't do anything. I just put the data in a data lake. I put the data in a data repository. I'm not capturing any metadata about it. It says column one, column two, column three when I'm trying to profile the data. What data is that? I don't know. That's a terrible experience, right? But if I manage, govern it better through something like cataloging, figuring out where it's stored, also understanding where, from an integration perspective, where does it move to and from, I've got a better experience. Now, but I want to make sure that that data, if it's PII, PHI or PCI, am I compliant with the data that's moving around? Do I have a glossary? Do I understand what that data means? Do I know what column one means? Maybe I should rename it and not name it column one. Perhaps I should have good metadata on the data that comes in. And then is it of quality? Is it good? Is 68% address verification good? Is it valid? Is it the way it's supposed to be? Well, it might be okay for some particular areas and some other areas, it may be awful. Maybe they need 98, 95%. Then I want them to do something with that data. So I've managed it, I've governed it, now I want them to do something with it. I want to make that available for the masses. I want to democratize that data so they can discover it. They can do things around their particular community. They can collaborate with others and then share that information. And then ultimately, I want them to take an action. What's the action I want them to take? I need them to consume it, do some reporting on it, do visualization, virtualization of the data. I want them to do something with that. So this was a silly example based on a grocery store just to kind of set the foundation of this is what having a good data governance and data management practice or program can enable. And when you don't have it, you have random brown boxes of lucky charms all over the place, not magically delicious. So how do we do this in InvescoSpeak? What's one of the ways that we're doing this right now and taking that particular example and taking it into InvescoSpeak? We have this concept of a data fabric. Show of hands, how many have heard of that buzzword in the last, and I thought so, data fabric. That is a hot topic right now. But I look at it as nothing more as a virtualization layer trying to help me make sure that I am buying the right lucky charms. That's what I'm looking at it from a data fabric perspective. But I want to weave all of this together for a seamless experience. How many of you have been to a genius bar at Apple? They seem to have all the answers, right? Like every time you walk in they're like they seem to know everything. It's impossible for them to know everything. I look at the data fabric as kind of that holistic Apple genius bar. They're a person that I'm gonna go to a seamless user experience within an ecosystem that connects all of this stuff together. But how in the world am I gonna ask somebody in any of those spaces to know that they should go into here or they should go into here? Do they even know what Aladdin means? I don't know if they know that. I can't expect them to know that. So what we want to do from a data fabric perspective is create this virtualization layer from a marketplace perspective and pull all of this together. And I'm gonna go back. Can't do that without doing these things first. You can't do anything fancy like a data fabric unless you have sound governance and data management principles and practices in place. You just can't. So if you want to do fancy AI machine learning and all that other jazz, you have to take time to build the foundation from the ground up. If AI machine learning and data fabric are the second floor, data management, data governance, privacy and security is foundational first floor type stuff. Cause that's how it has to be. Now I've said all of this. Now how do I know that some of this stuff is actually getting done? And this talks about our three areas from an oversight perspective. We have our execution groups, our data boards and our cross-domain data council. The top of the house, my boss sits up there, Jim Tayo. He's our chief data officer. But also remember I mentioned those data domains from across the top. Those individuals also sit in that cross-domain data council. They have equal skin in the game because remember I'm asking them to be accountable and responsible for their data. So they've gotta have a key seat at the table. So this is a monthly type meeting. All of these are really monthly type meetings or as needed. The data boards, these right here, those data domain officers that sit there, the ones that are also equal seated at the table from a cross-domain perspective, they have equal footing as it relates to the data boards as well. They have their own data boards that they go out and talk to. They're made up of their stewards. They're made up of their custodians. They're SMEs. They're data users. They're data consumers. That's all of the individuals from these two areas right here. And then when we have some sort of execution that needs to be done, maybe there's some sort of project, program, or kind of one-off, hair-on-fire type event, that's when you're gonna see these all hands, hands-on keys execution groups. At the bottom, enterprise data governance, responsible for enterprise oversight, optimization and data integrity, timeliness, usability and trustworthiness. We sit foundationally on all three of these areas. So I have a seat at this table as well as many of the other meetings I attend. I attend those other meetings. Second line of defense to make sure that we have maintenance of quality as it pertains to critical data elements. You cannot govern everything. You can govern things around critical data elements. Start with the most critical, some sort of criticality assessment that you wanna do and work your way down, top down, okay? And we're accountable for that DQIS, the Data Quality Integrity Insufficiency Group. Last but certainly not least, and this will be the last slide that I leave you, maybe, is this Data Quality Integrity Insufficiency. It is a risk group that I chair, specifically around asking these questions. Is the data adequate for my needs? Is it sufficient enough? Is it accurate and reliable? Storage and availability, when can I get it and how do I get it? From a protection perspective and an ethics perspective. Are we ethically doing the right things with our data? How long should I keep it? All of this ties together that program from a data governance perspective. Why is it important? Large data's coming in, digitization, all of the things that we've experienced in the last 24 months is driving the need to have a dedicated data governance and data management practice program in each of your specific areas. I can almost guarantee, but I wouldn't hold any money, so please don't bet me any money. I can guarantee that if you have a robust governance and data management program, you can absolutely change the world as it relates to your particular organizations. So that's the last slide. I have 14 minutes left and I wanna make sure that I leave some time for Q and A. And I also wanna take a drink of water. Don't be shy. Just raise your hand if you have a question. We've got a couple of folks with microphones. Got one back here on the right, back here on the left. Okay, right behind you, Carla has a mic. Thanks, that was great. I do have a question. You used the term shared services and a number of times. And I've done some work in organizations where data quality, data governance, data management all kind of folds under that concept of shared services, which in that context, it is viewed as meaning nobody really cares about it because it's shared, so nobody really owns it. So how did your organization create the engagement and ownership or shared ownership in shared services? Yeah, I know that's a great question as it related to the shared services question. So yeah, it's kind of a taboo thing to say shared services, mostly because like you're saying, like, oh, nobody cares about that, I don't have to do that. But really, it made it very, very easy when we went in from a governance perspective using kind of this framework that I talked about right here and going, hey, by the way, your data quality is X. Is that okay? If the answer is yes, then I go, okay, fine, that's great. Or I can go, by the way, you're over retaining your data. Like it was very easy for me to find data risks or data gaps for them and go, is that okay? If that's okay, then we'll move on. We'll move to the next particular area. And then we shared that with their senior leaders. Do you know that you're retaining data for 26 years? Oh my gosh, no, I had no idea that I was retaining it that long. What's the policy say? Well, the policy says seven years. Well, that seems like a pretty big miss. Yeah, you probably should do something about that, but they ultimately have to be the ones that sign off from an accountability perspective so they either have to accept the risk, which if they wanna accept the risk from a governance perspective, probably not what I would do, but then again, I don't sign their paychecks. So if they wanna make that and accept that risk, totally up to them. My recommendation is always to, well, let's mitigate that risk. Even if that risk takes four years for us to mitigate, let's start to put the process and steps in place in order to really get rid of that over the course of four years. If you're in a heavily regulated industry, a regulator coming in will love to see that you have something in place. Even if it takes you four years, they're gonna love knowing that you recognize the issue and that's gonna take four years to get it done. Great question. I think I had one over there. It's a great presentation. Thank you. Thank you. So from the budgetary perspective, how big is your team is and how much data they are able to govern? So it's like your team is like 10 people and you're governing millions of data assets or something like that, just to give some perspective. Yeah, yeah, yeah. So I can give a perspective on the EDO and the data governance. So I'll do both. From an EDO perspective, we're a team of about 81. So we're not a very large team by any stretch of the imagination or maybe that is a large team. I see some chuckles out there like, I was gonna say, and it's a good question. Who thinks 81 is large? Okay, well, I have to say who thinks it's small? Yeah. Hey, gotta be about, can't buy yourself, man. Okay, so 81, maybe that's a lot from the entire EDO perspective, but from a governance perspective, I have 23. You didn't know how spoiled you were, did you? I did not realize how spoiled I was. Well, crap. So from the EDO perspective, we have insight to almost all of the data assets as it relates to distribution investments in corporate services. That's multiple petabytes of data. So a lot. And I guess the team of 81 is pretty good. So I'll take that. From a governance perspective, we are starting with, again, I think I had 20-some team, half of the team is in India. And so our team has oversight into all of that data as well, but we're starting from a critical data element perspective. So from a critical data element perspective, probably 20% of the most critical data, obviously that number will continue to grow as we go further down the list as it relates to criticality. I learned something new, the 80-some from an enterprise data office. I'll let Jim know. That's what I'll do. I'll text them right after this. Well, that line of Q&A actually prompts me to ask this question because everybody thinks that their company, their organization is different, their needs are different, somehow unique. And yet, talking to most consultants, they say the same thing happens in every... You're on the customer side of things as opposed to being a consultant. I mean, but you've moved through a variety of organizations over the past 10 years. How do you do... It's the same in every one. It's a lot of the same stuff. That's the thing, it's like every consultant that you'll pay for, a lot of the stuff, quite honestly, a lot of the stuff that I learned came from this particular conference that I was able to put into practice. Sorry to our consultants out there or anything like that. Not saying you shouldn't pay for a consultant because they all serve their purpose. Don't get me wrong. That's not what I'm saying. What I'm saying is a lot of the stuff you can find, it's, you can pretty much use it in any industry. I mean, you really can. And that's probably the best thing that I can describe is the things that I've talked about. I learned them from Googling. I learned them from this. I did learn some from consultants and things of that nature. But a lot of it can really fit into any part of any organization that you have. And just to put the 81 in some perspective, you work for an investment organization and that is pretty highly regulated. Yeah, very highly regulated. Worldwide. So a lot of those folks are doing things that are sort of driven from a need for the business to be compliant with those regulations. I have an entire data regulatory team that sits on our data governance team that does nothing but regulatory reporting for very specific, so a lot of ours are European. So a lot of the reporting that we do from a reg reporting perspective, we have to cross the T's, dot the I's before we send it to anything like that. That's just from an EU perspective. Okay. Kala, we have one behind you and I'll get this gentleman up front. So take this question up front first. Thank you for the presentation. Yeah. Good question I had. I know you a little bit talked about the training, right? So how are you managing the data literacy as part of your data governance within your organization? So the question was how? Is that kind of quiet? The question was about how do you manage data literacy? Yeah, the data literacy in terms of the training and education. One of the things that we do and one of the services that we provide out of the Enterprise Data Governance Office is specifically this training around something we call workshops. So every single month for what feels like the last 11 months has been nothing but training and education on a very specific need. So one of it might be standing up a stewardship. That's one of the workshops that we have. Another one we have is critical data element identification. What do I have to do to govern critical data elements? That's another workshop that we have. Another workshop, which is actually a two or three parter is around data quality. The first part of that workshop is what the heck is data quality? Because you would be really surprised to know that not a lot of people know what data quality actually is. So we have the first part is what's data quality? And then the second one is, okay, what do I do? How do I write business rules in plain English? Then after I write business rules in plain English, then what do I do? And then based on the technology that we use, we input that into the technology. And so this concept of workshops is really where it comes in to be. And then after each of those workshops, they get a syllabus. Just like if it was a college course, they get a syllabus to say, you need to bring this to the course. This is what you get outside of the course. And that is all published out on our SharePoint sites. Okay, back right there. You just raise your hand so it's, yeah. Yep. Can you hear me? Yes. So thank you for a wonderful presentation, Scott. So I have a two-part question. Yep. First part is, given that, you know, you have a healthy size shared services organization, where does the budget set for your organization, is it with the CDO office or is it funded by the business units? That's the first part. Okay, it is actually funded at the top of the house. Perfect. So when it comes to buying, you know, let's say another data asset or a product or something, you have the full authority to make that decision. We do that in conjunction with the business, basically to say, you know, we would say, hey, do we have this data that you're trying to purchase somewhere in the other part of the organization? It's our responsibility to kind of help figure out if that's the truth. And then if we don't, then we facilitate that conversation. I'll go back to the DAS organization, specifically, come on. Right here. That's where we're having those conversations. If we have a vendor come in to say, I want to purchase third party data, that's where that conversation comes in. Perfect, so that was gonna be my second question, which is, let's say I'm a marketing department, I'm looking to do a marketing campaign and I need a specific type of data for that. So they would come to you first before they make that decision. That's correct. Okay, thank you. Okay, question front left. Yeah, it's interesting. How many stewards have you been able to build up in your community to support your data governance professionals and any secrets about getting them on the team? Yeah, so a lot of them, so in terms of raw numbers, oh boy, I'm gonna try to do math on the fly more than 50, 60 across the organization. And now a lot of them didn't realize they were stewards because they were doing activities that were like, no, this is just other duties as assigned. Well, no, they're like, you're actually a data steward. And so that's the other thing is like, really understand that these folks are probably out there already doing this work. You just say, no, no, no, I wanna give you credit for the work that you're doing now. Like I wanna actually give you a fancy title to say, you're a data steward, you actually do things. And so 50, 60-ish stewards across the organization, but as we go more and more and more from a critical data elements perspective, we might pick up twosies, threesies here because obviously it could be a new data asset that we just haven't got to from a criticality perspective. So are all the stewards within the business? All the stewards are within the business, yep. Okay, we have another one front left, which there one at the back, no? Okay, again. This is just a follow-up on the literacy and how do you educate people? How can you put them in that classroom? How do you do that? How do I do what? How do you bring them to the class? Oh, how do I get them to attend the class? Yeah, because they've got other stuff to do. Yeah, so that's been probably the toughest part is what brings them to the classroom, right? So a lot of the times we work with those domain data officers to say, hey, we're having a class coming up in the next six weeks. What people do you need us to attend or what people do you need to attend? And they're gonna say, I need, and I'll go to this particular slide where we talk about the data fabric. I need data stewards specifically from Aladdin, Cadis, and CRD. Who are they? And then we go out and talk to them and say, hey, you've been identified as a potential data steward. Do you do the following activities? And then they say yay or nay. And if they say yes, we schedule them into a virtual classroom training, but we have pre-meetings with them to say, this is exactly what we're trying to get on board to do. You already work with so-and-so from the domain data office. This is what we're trying to get you to do. Yeah, so, but you started with that person's manager who bagged this up. Yep, we did. We started with their manager or their manager's manager. Okay. Halfway back on the left, I believe. Yep. So just along the lines of the data steward, did you incorporate the data steward role as part of their job description then? Like how did that work? Working on that now. Far left on the back, at the back. Hey Scott, thank you for the presentation. So it sounded like you were pretty recently at nationwide and now you're at Invesco. So I'm wondering how long has this taken you to get to this point and is it possible to go too fast or too slow, I guess? So this is where I can actually set back and realize when I had a team of 20-something, it made me go a lot faster. I was able to accomplish this in just a little over eight and a half months. If I didn't have the people, I wouldn't have been able to go that fast. Let me be, this is where I'm like, I'm very lucky. Like it just hit me how lucky I actually am. But honestly, you can, this is a multi-year effort. I'm very lucky, but this is a nationwide. It took me four and a half years. Okay, we have a question on the left and then one in the middle and we'll have to wrap it up at that point. How do you, how do you create excitement from being volunteer to come to one of your workshops to actually embracing data governance? Wow, a lot around this idea of getting people excited about data. I have a very aggressive communication plan. Like I do videos in one of the presentations nationwide when we did the DGIQ, we did this concept called data in 60 seconds or two minute Tuesdays. So we constantly talk about the benefits of a data governance program. I cannot understate or overstate, overstate. That's the right word. Overstate is enough. You have got to be a communication and marketing company that happens to do data governance. You have to, have to, have to. Because otherwise, you're never gonna get these people to buy in because they're never gonna understand what the heck is a data governance and why do I need to do this? And it's about telling them the value of doing these things and actually showing tangible proof what this value is. I was able to get your report faster. I was able to help you find where your data was tracking to and from faster than you could before. Perfect example. We had a program that basically said, I don't understand how I retire this data. Like I gotta retire this thing, I gotta decom this thing, what do I do? And so from that perspective, one of the things we were able to do is go in, showcase the data lineage, showcase the data quality and go, when you shut this thing off, this is what happens. And then they're like, oh, so that's part of data governance? Wow, that's pretty slick. That's really cool. I didn't know that. And so it's really talking a lot about the program that you have. Okay. And right in the middle of the room there. Hey, Scott, I'm a department of one. And just starting out. How many people think one is a lot? So I'm just starting out this big journey ahead of me here. And I'm thinking that one of the things that I probably need to do is do a data criticality assessment. Is there a tool that you used to do that? Excel. I would honestly, the reason why I say Excel, it talks about like I would go out and have conversations with the business develop the relationships with the business to say, what is your critical data? What are your critical data assets? And use Excel. And the reason is it's approachable from the business perspective. If you put some fancy tool or technology in front of them, they're gonna just glance over but Excel and PowerPoint for whatever reason are really approachable. And really start to build the relationship with them to say, tell me what your critical data assets are, tell me what your critical data elements are, and then put them in an Excel and start there. Almost every technology out there does some sort of bulk import as it relates to Excel. Yeah, we're starting to do that now because of the fact that we're in a business that's heavily regulated. And it's growing like leaps and bounds. We tripled our staff within the last year. And so we are also looking at going beyond the state that we live in and going further into other states. And so then my other hat being the privacy officer as well is starting to look at all the data elements from that particular aspect. And so I'm just trying to get my arms around what particular tools that could be helpful to do this. But we've been using Excel because in the banking industry, Excel is the prime tool to be used. And so that's helpful to know. But it sounds like the best place for me to start would be the critical data path and... Yeah, I would. My recommendation would be start there because all of your service offerings that you have from a data governance perspective, your lineage, your reference data, your data quality can all be banked off of the critical data elements. Even your asset inventory. You can do an asset inventory to say, this is the totality of my landscape. You can do all of that based on the criticality and the critical data element exercise. Uh-huh. So even though it's a bit playful with that question, we get a lot of people coming to us now. Seems data governance is moving into small and medium-sized businesses at an increasing pace. And we actually get people coming to us saying, you know, I just, probably many of you are in the room saying, you know, I've just inherited this. I'm doing this and that and something else. And I've been given data governance. So it's maybe a quarter or a half or less of their role. So yeah. I'm lucky. Yeah. It hit me. We're not going to have time for any more questions. I'm sorry, but Scott will be around for a little while. Yeah. To chat with him. There was one thing I wanted to ask you. So when Jim reluctantly informed us that he was unable to make it, he did say, you know, it's probably just as well cause I just get in the way of Scott. He does. Sometimes. So. So I get on a roll. That's what it is. I get on a roll. I get really passionate. I start talking with my hands and then my hands will go everywhere. Well, I think he was, I think he was saying not just in presentations, but in the job that you do, you know, how do you work with the CDO office? How do you work with Jim directly to enable or, you know, when he says get out of your way? What does he mean? A lot of times it's like I work with my peers. So the peers from this particular area. So right here, obviously Jim said to the top of the house, but I work with my peers. It's my job to make Jim's job easy. And so what I do is I take it upon myself to basically understand what are the goals and objectives of my peers and where does data governance interject themselves in each of those different areas? Like the data fabric is a perfect example. The data fabric is actually out of systems and architecture specifically. So those particular leaders are leading that initiative. So where does data governance fit into all of this? That way when Jim has a question and goes, hey Scott, you know, I've been thinking about, you know, maybe you getting involved in the data fabric. I usually go, I'm about four steps ahead of you. I'm already talking with this person and this person about how data governance fits in here. We're already capturing the metadata from this system, this system and this system. So is there anything else or, and like that's typically how the conversation goes, but I look at it as a part of my job is to make his job a lot easier. And he casually jokes like, I get in your way by asking you these questions for questions you already know the answer to, but that's just, I've worked with Jim a long time. And so I get really lucky that way. But I would say take the time to develop the relationships with your peers, with the peers out in the business, peers out and obviously I'm very lucky to have peers like this. But if you have other ones out in the business, take the time to really learn what are some of their objectives and where could data governance make a difference for them? That's what I would say is the big way. All right. Scott, I think you've given us some insight into what has to be the closest thing to best practice that I've seen. Thank you. Thanks very much for bringing your story and we wish you all the best. Thank you very much. Appreciate it.