Hello, everyone, and welcome to this webinar. It's the Accutics Data Leaders Series. And today we have an amazing guest, George Firican, and I'm very excited to welcome him in a minute. You might notice that I'm not our usual host, Kristian. Unfortunately, Kristian was not able to be here today. So that means that I got a chance to be a host here. So I'll do my utmost best and see if I can steal the role away from him. No, just kidding. But today is all about data governance. The Data Leaders Series is all about data governance and how companies and thought leaders are succeeding and sharing best practices about how to get ahead with data governance. And today, as I mentioned, we have George Ferrikan on. But before I intro him, I just want to introduce you to a few house rules. First of all, I want to introduce myself. My name is Casper Frid. I am the VP of Growth here at Acutix. And I'm very excited to talk more about data governance and to welcome you as an amazing audience to this webinar. To your right of the screen, you will see at the bottom, there's a chat where you can write chat messages. You can put in emojis. So let us know where you're from. Let us know if you have any conversations internally in the chat. Please keep them going. They are always amazing. On top of the chat, there's a question part where you can type your questions and George will make sure to answer these at the end of the webinar. We'll do the Q&A. When you enter your question up on top, make sure to press enter or submit. Otherwise, it won't come up. So we've seen that before. Also, on the top, what we'll see is the polls. In a second, we'll just launch a poll to test this all out and see how you guys are doing out there. So this will be where you find the poll as well. And let's try it out, actually. I'll just launch it here. So the first poll is starting now and should launch in the right side of your screen. It says, what is the most pressing challenge of data governance for you today? Please put in your answers. I will keep the poll open for a while and then we'll come back to it and see if it resonates back to the points that George are bringing forward. With that said, today's guest is a very special guest. And I've been looking very much forward to welcoming George to this webinar. George is a host of Lights on Data. Data Show and Lights on Data.com, like hosting a lot of insights into data governance and how companies can succeed with this. I'm just going to read out loud here because it's quite impressive. George is recognized amongst the top five global thought leaders in big data, data disruption and innovation. He's a top voice on LinkedIn. He's recognized with international acclaim for sharing data topics and making them tangible and actionable for companies, which is essentially what it's all about, right? Lastly, George is also working closely together with organizations on elevating their data as a true asset that they can use to grow business. Talking about business growth, this is what the webinar is about today. So with no further ado, I'd like to welcome George to the webinar. George, welcome. It's so nice to have you here. How is it like hearing an intro like this? You must be a busy guy. Hey, Casper. Very nice to be here. Thanks so much for having me. Really, really appreciate it. And I was looking forward to this as well for some time. And, you know, good to see you again. How does it, you know, is to be on the other side? It's a little bit nerve wracking, I think, because I'm usually the one asking the questions. But I love being on this end as well. I love doing presentations and being in front of people and meeting new ones and learning from each other. I think that's all there is about, you know, we're always learning from one another. Yeah, I fully agree. And I really appreciate this. And it all actually also came together after a chat where we talked about some of the connections between business growth and data governance. And we'll get way more into this. But before we get started, I would like to ask you, George, as a top expert, can you please give us just a short intro to how you see data governance and what data governance is and why it's important for you? For businesses? Yeah, yeah, I would love to. And I'll just share my screen here a little bit. I think a visual would help a lot. I do want to preface, though, that I think, especially within the data community, there's no clear aligned definition on what data governance is. So and especially its relationship with data management, there's different variations and kind of depending on the industry and the different contexts that you might be in. So if you have a different view of what it is and what I'm going to showcase you here, you know, let me know. Let me know in the comments and, you know, happy to go over all of that, which is kind of ironic. I feel that data governance doesn't really have a standard definitions because that's one of the things that's trying to accomplish, you know, put together standards among other things. But all right. So let's start with this simple concept, because we hear about this a lot. Right. We we hear the fact that we need to get value out of our data. Because its value is worth a lot. You know, it's often what gives data driven organizations that competitive advantage. But in order for our data to turn into value, well, you know, it's not really a simple straight line. It's not a simple path. A lot of things need to happen along the way for that value to come to fruition. So I'll give you a few things of what needs to happen. It needs to be integrated, transformed, interoperated, analyzed, modeled, disseminated. Again, just to name a few of the things that we need to do with this data. Our data also needs to be cleaned. Be made consistent, accessible to the right people and systems, secure, defined and understood. In other words, our data needs to be managed. So we need to manage our data if we're going to get value out of it. If we're going to have consistency and accuracy in our marketing reports, to have that single view of our customer, to get actionable information that informs our business decisions and enable us to be a data driven organization after all. You know, back to all of the things that need to be done to it. So I said it needs to be clean, consistent, accessible, secure, defined and understood. Well, how do we know what this desired behavior should be? You know, behavior of having clean data, documented metadata, categorized and classified data and so on. We need some policies. How are the steps to clean our data, to make it consistent, to provide access to it, to secure it, to define it? Well, we need some processes that we need to follow. How do we ensure consistency in our cleanliness, in our definitions and so on? We need some standards as well. And who's going to create all these policies, processes, standards and rules and definitions? Who's going to approve them? Who will maintain them? Who will enforce them? We need some roles and responsibilities. So everything that you see at the top there in that triangle, that is data governance, which is actually part of data management, which is kind of this entire thing here. Data management is the business function of planning for controlling and delivering data and information assets. So just to give you a little bit of context and about that relationship between data governance and data management, which again, this whole thing is data management and data management is this business function of planning for controlling and delivering data and information assets. So overall, data management contains these 11 components or knowledge areas as identified by DEMA. And DEMA stands for the Data Management Association International. It's a well-known organization throughout the world. And they're supporting the standards and the best practices when it comes to data management. And data governance, it's actually one of these 11 data management knowledge areas, and it sits right in the middle. And it sits in the middle because it has a relationship with all of these different areas. It has a relationship with data quality management, data security, metadata, reference data, data integration, data storage and operations, all of them really, which sometimes it kind of creates a bit of a confusion, I know. But just to recap really quickly, data management is this really overall umbrella that's containing all these different knowledge areas as identified by DEMA at least, with data governance being one of them. We see that in the middle there. And data governance is just a discipline that provides the policies, the necessary policies, processes, standards, roles and responsibilities that are needed to ensure that data is managed as an asset to get that value out of it. I often quote John Ladley, and he's mentioning oftentimes that data governance is a required business capability if you want to get value from your data. All right. So yeah, we need that. We need that as a foundation. So let me know if you do have any questions about this high-level overview of data governance there. Oh, it's great, George. And I think actually this is the thing that we see on our side, working with data governance software. We actually see that the definitions of this is so hard, and it's so hard to talk about what it is, how we impact it. And we'll get closer to this in a minute. But going into the ROI part of this, I wanted to ask you, what are some of the most overlooked aspects in this or in other fields that you see that leaders, marketing people, even board members are overlooking in this relation? Yeah. Yeah. Thanks, Kasper. You know, I'll probably sound like a broken record by doing the webinar because I'll mention data governance a lot. And that's really what, at least from my perspective, and I know I have some bias there, but that frequently overlooked aspect is that data governance. And many founders, you know, marketers kind of dive in headfirst into data-driven tactics without maybe establishing the strong foundation for data governance. And oftentimes, it's kind of rightfully so. You understand where they're coming from, why this is overlooked, especially for a startup company. I mean, what's important first is to get that ROI as soon as possible, to get the revenue, as high as possible, to build that customer base. So they obviously have a lot other areas that they need to focus on. And then data governance kind of follows, unfortunately follows long after. And same for seasoned and established organizations, companies. It's not the first thing that they focus on. And the problem is, you know, without this data governance in place, it's kind of like trying to build a skyscraper on shifting sands. And what happens when you do that? Eventually, cracks will show at the very least. If not, you know, it could even topple over. And as you can imagine, as you keep on building on this, you know, shifting foundation without data governance, as you keep on building, it becomes more and more expensive to kind of fix the foundation itself, right? It's, you know, much harder and much more expensive to do something right if your skyscraper is like, let's say, 20-story high versus like a two-story building. And also the risks are increasing every time you're adding one of those floors on top of it. So, yeah, I think, again, you do need that data governance to ensure that consistent data quality, to provide those clear metrics and those definitions and standards. And without it, really, growth strategies often falter because they lack the structure, the consistency, the clarity, the efficiency to scale sustainably. I also like to say that kind of, data without governance there, it's kind of like a compass without a needle. You know, it might look promising at first, but it's not going to get you anywhere. No. And that's sadly what we see a lot in the market, like that this idea of data governance is not always shared or understood fully. And why do you think that is? Why do you think that with this being kind of obvious and you say it sounds like a broken radio, because it's something that we can say again, again, and you say again, again, because it's important. Why do you think that businesses are still not prioritizing at wider scale this aspect? Because I see marketing starting to prioritize this and we'll get into this, but at a wider scale, why don't you think this is a priority? Yeah, you know, maybe kind of if we're looking back, you know, when organizations began adopting data processing technology, you know, systems were built to support transactional business processes to make them less labor intensive. So, you know, like processing or recording an order or balancing the journal ledger and like a bunch of accounting functions. That was kind of usually the focus on the finance accounting piece. And data was more of a byproduct of running a business and had no value beyond the transaction itself. So I think it's only, you know, starting with the 90s that data was trying to be seen a lot more valuable. And I don't know, you might remember that saying that, you know, data is the new oil, which I know it's some people are arguing that, well, no, it's not. It's such a bad comparison. But really, with this guy who was a data analyst at Tesco, Clive Humby, what he was trying to say was that data is valuable. That's kind of the message. Yes, maybe the analogy comparison is not the best, but that's what he was trying to say, that if you mine data, you can actually get a lot of value. Out of it. And that's kind of when data governance started to gain some traction. But as systems became more complex and more than one system was brought into the organization, and especially as an organization kind of started to share and consume data from other systems and other organizations, that need for data governance became a little bit more apparent, but it was always reactive. So I think some companies are still catching up on this, even, you know, 20, 30 years later as well. And oftentimes, I think there's always two sides of data governance. There's kind of the stick and the carrot, the stick being, you know, there's all these things around privacy and legislation and GDPR and whatnot that you need to comply, otherwise you're going to be fined. And then the organizations are feeling, okay, well, it's something that I have to do, I don't really want to do, so I'll just do the minimum thing that I need to do there to invest. But I'm a bit upset about it because it's wasting resources on me, and they're not focusing on the carrot piece. And I feel that the carrot is really what's bringing the value into the organization. So I don't know. Yeah, maybe it's kind of, you know, if you think about it, I feel IT was facing a similar issue back in the day that they always had to prove their worth. You know, companies like, well, why are you going to invest all this money in our IT department? Right now, I think it's a little bit more accepted. You know, it's a given that each organization would have some sort of an IT department with some resources there. And overall, if you think about it, nobody cares if things are working well. As soon as something breaks, you know, that's when, oh, IT. It's IT's fault, IT's problem. And same with data and data governance. So and now you open it a bit. What are the symptoms that you see and you think are significant? Because this is really the value in it also. What are the symptoms that you see when you don't have the good data governance? Yeah, I, you know, kind of take your pick, but to name a few inconsistent data definitions, inconsistent business term definitions, like customer, for example, trying to define customer, it's really a hardship. Try and ask two different people in your organization how they define customer. And regardless if they belong to the same department, let's say marketing or two different ones, they will probably come up with three different definitions between them two. Fragmented processes and really a pervasive lack of trust in data accuracy. I think this is the biggest thing that we're seeing. But also the real damage kind of lies beneath all of this as well. You see teams that are wasting time reconciling reports and looking at all these different sources of data and trying to make sense of it and say, well, are they, are we looking at the same thing? Because that's what it's saying in our metrics there. They really the same things. Leaders then doubting these metrics saying, well, you know what? I know for a fact that the views, the video views that I'm seeing from YouTube doesn't mean the same thing as the LinkedIn views for the videos. So are you trying to trick me there? And opportunities really slipping through the cracks between, because decision making is kind of paralyzed by all of this. I think when data governance doesn't work, you kind of often hear phrases like, you know, which number is correct? Can I trust this data? What is your definition for a sale conversion rate? So yeah, yeah. When there's this uncertainty and unclear ownership and misaligned processes, I feel like marketing efforts are becoming reactive instead of strategic. And the resources are kind of squandered on chasing insights rather than delivering the impact. Yeah, absolutely. And it's the same. And we don't have to open that closet now, but it's the same, like the whole attribution game, right? It's about, are we trying to show how good we are? Are we trying to create impact in our company? And the whole setup is quite interesting. And actually from what you say, let's say, have a look at the poll here because I think it's quite interesting. And George, are you seeing the poll here? I'm not. No. So, okay. We have a glitch. We'll get it up. But I can tell you that 67% of the viewers live here says that lack of data quality is the main challenge. Yeah. And I'm not surprised. You know, there's many, many different drivers as well for why do you want to invest in data governance? And there was a report, I think, from TDWI that came out last year. They kind of put this report every year. But data quality was still number one reason why organizations are investing in data quality, in data governance. Yeah. And it makes total sense. And that kind of leads me to the next topic that kind of opened this whole conversation when we started talking about doing a webinar as well. We did look into the combination. We saw that some of the strongest companies that outperformed industry growth were actually quite good at data governance. So we decided to look into the combination between data governance and business growth to see if there was a connection in these two. And what we found was actually that there was in many cases. And those who actively prioritize this had it as a competitive advantage, like an edge to their whole setup, because they could kind of turn the keys, as you say, and from the examples that you're giving. So they could act quicker than the competition and they could actually get ahead on a lot of areas within marketing. The funny thing is that many of them also come out as thought leaders in their own organizations, because when you start proving these things and you can move at a rapid pace, and we're not only talking for big data models and stuff like this, but we're also talking about daily decision making, other departments start coming to them and asking them for advice. Is this something that you see with the organizations that you talk to as well? Is this a trend that you see translate into the daily businesses? Oh, yeah, yeah, absolutely. I mean, you know, data governance really provides the visibility that's needed into both data and business operations. I feel like there's this kind of one-stop shop for resolving data related challenges, or depending on that maturity level, it tends to become a one-stop shop. And, you know, as I was mentioning before, it kind of ensures that the technical and business metadata, they're well-defined. And that's very important. It's making data understandable and actionable for everyone. And without it, issue resolution and prioritization also kind of become a guessing game too. And of course, the timeline is accuracy, quality of the data also suffers. I think having those common definitions among different departments may be one of the biggest wins. There was this survey a few years ago from the International Data Consortium, and they were mentioning that the average information worker, which is really anybody that's working with data or benefiting from that data. So even kind of looking at a report, they will be classified as a information worker. So that's almost everybody within the organization. But they were estimating that they're spending about, I think it was up to 10 hours a week, just searching for information and data and making sense of it and trying to understand it and confirm it. So oftentimes, if you don't have this data governance foundation and these definitions in a central place that you know it's trusted, it's maintained, it's curated, then you're going to keep on having all these meetings to clarify things, to message your colleagues and phone them and have conversation and hours and hours of meetings. And maybe that needs to happen. But once that happens and you agree on something, put it in these tools, have it defined, have it approved, and then you can just refer back to them. Because what happens, otherwise, one year down the line, you would have a similar conversation maybe between different people within the organization. And you go through the same thing over and over again. And 10 hours per week per person, that can add up to quite a few money spent, for sure. Yeah. And it's so interesting because that has also been the hard part, like tying these two things together. But talking about naming and a common understanding of this, we talked about your good friend and former guest on the Data Leaders webinar, Brent Dykes, who released in Forbes some time ago his data analytics marathon analogy, which is quite interesting, looking at the steps from where we collect the data to where we actually take decisions from the data and a lot of the steps in between. And his analogy on that it's a marathon is quite accurate because it's a long way. And his key takeaways is really that 95% of all companies collect data. Only 5% are able to take decisions from the data collected. And in the trail from collecting it to taking decisions, like we have data preparation, data visualization, data analysis, insights communication, which are difficult tasks within this game. Do you see that the data language, one, is impacted by data governance? Like, so it's also adapted. We can create data governance, but is it really adapted? And secondly, do you see or how do you see that if we have proper data governance in place, can we make this marathon more of a sprint, which is often needed in the market in 2024? Yeah, yeah, I like that the marathon sprint analogy. By the way, I think Brent Dykes is amazing and he writes great content on Forbes and not only, you know, so follow him on LinkedIn, his wealth of knowledge. And we actually, we were on a call last week and not about us, but I like the coincidence of it all. So yeah, absolutely. I think strong data governance can turn the data analytics marathon that Brent was talking about into that much more faster, more efficient process overall, the sprint that you mentioned. How so? I mean, by standardizing data, describing and defining it, making it consistent of high quality and therefore removing some of those bottlenecks, it's also reducing the time spent on manual data preparation and interpretation, especially. You eliminate those hurdles that kind of slow down progress. When data governance is in place, marketers can trust the data at hand. You know, and then shifting their focus from fixing issues to uncovering insights and taking action to, you know, the marathon sprint analogy. I don't think that data governance would get you there on a shorter route per se. So you would still have to go through those steps that Brent was describing. So it's not about cutting corners. Maybe it's about kind of paving a direct, reliable path to the finish line, if you will. And, you know, and even when you get at the finish line, I want to preface that you're still going to run. Maybe you're going to run in place, if you will, because there's always something to be done. So data governance is not a project. It doesn't have that end date. It's a program. You need to keep on investing resources into it. And yeah, overall, it really ensures that at every step in that analytics process, that collection, preparation, visualization, analysis, everything really runs like clockwork. Without it, teams would be wasting time, kind of second guessing those numbers, piecing together those fragmented data assets. And with good data governance in place, those delays disappear, and your organization can act with agility and confidence. And you need all of that to outpace competitors. And yeah, I feel like it really transforms analytics from this marathon of frustration, if you will, into the sprint where it's success. Yeah, it makes a lot of sense. And actually, here at Acutix, we've been on quite a journey on this as well. And one of the interesting findings that I've seen in this journey has actually been what you were talking about, that the marathon to sprint becomes easier when the results becomes more obvious, when we can start talking about the steps that the data goes through, because then people know what to expect. And a lot of areas outside data analytics and marketing data and stuff like that, like sales needs to understand these numbers as well, if they need to have effect, if they need to be actions in the commercial business, which is often where the business effect is driven. So I think it's quite interesting what you're sharing here. And I couldn't agree more that setting up these steps takes something, but once it's there, it kind of scales because then you have the data and you have it flowing, so it becomes faster. And one of the things that we're seeing is that marketing in 24, and for sure, even more in 25, is very much about swift actions, rapid change, like figuring out, like we have an overall strategy, but there are a lot of fragments where we need to translate this into more rapid actions. On the other side, when we talk about the data marathon, it's a very strategic approach to it. And it's a very strategic way of approaching it, which is a longer process, which is maybe quarter over quarter, year over year, even longer, like three years, five years, right? How do you see these two interacting in the market and in the data governance game, actually? Yeah, yeah. And to kind of get that balance, right, between the long term and the rapid action piece. Yeah, yeah, that's definitely the challenge, especially in the fast changing markets that we're seeing nowadays. And so first, you know, let's start with the foundation. And that foundation, to me, is that data governance they need to have in place. And then I'll provide some steps on how you should take on or maybe the things to focus on. To kind of achieve this balance overall. So again, the foundation being that the strong data governance, because it provides you the ways to maintain the data quality or even get to it in the first place and then maintain it, consistency, accessibility. It gives you that framework where speed and accuracy aren't really mutually exclusive, in my opinion. So again, from a marketing perspective, your marketers don't have to waste time validating data or resolving those inconsistencies. And data governance kind of acts like that safety net that ensures that data driven decisions remain reliable and consistent. So once you do have this data governance piece in place, which, again, it takes some time, maybe six months, maybe a year just to get the foundation right. Why? Because you need to have the sponsor in place that supports you. You need to set a direction for it. So you need to understand the drivers and maybe in the majority of the cases, data quality will be the driver. But you also need to tie that to the business strategy and the long term strategy. With the quick wins in place. But, you know, nobody really wants to have good data quality just because it needs to be tied to a business action. It needs to be tied to a business outcome. So you need to always make that connection because then you always need to prove it within the organization. Why are you spending money and resources on this? Well, it's because to help us do this, to help us improve our sales conversion rate and have more customers and overall improve our revenue. And so forth and so on. So you need to tie it in. And then ideally, you have some sort of data governance body that makes some of these decisions. So there's a strong collaboration between the business units and then IT or data teams. And you have that collaboration. And as a decision maker, too, when you're describing and defining something like customer, it's usually this group that defines it for the organization. And then you just put together some high level, at least to start policy standards, processes, and you choose a domain to focus on. In this case, it would be maybe, let's say, the marketing domain. So data that's pertaining to marketing and taking from there. So that's kind of the foundational, let's say. But, you know, that's not enough. You need to build on it. So once you have this data governance framework in place, you kind of should focus on automating data validation and quality control. So leverage tools as much as you can to ensure that your data is accurate, it's complete, ideally in real time. So you're not manually fixing those issues when quick decisions are needed. You have to define those data priorities constantly because not all data is equally critical for every decision, for every business decision. So identify those key metrics, those insights that have the most impact, and then ensure that those data pieces are governed, you know, with the highest standards and according to your data governance program. I do recommend having some sort of a dashboard set of alerts that's equipping teams with that real time information and automated alerts that kind of flag issues or even opportunities. And, you know, definitely invest in fostering a data culture, but also culture of trust. So train your team on the data governance program overall. What does it mean? Why is it important? How does it tie into what's in it for me, for the unit, for the individual, so that they can start having confidence in data? I think when you start this roadmap, when you don't have data governance in place, and as we've seen a lot of people have an issue with data quality, there's an overall skepticism and second guessing on different reports, different decisions that are being made. Different units are creating their own shadow databases to take the matter in their own hands, but that's creating even more problems for the organizations. So yeah, you need to invest in this for sure. It makes a lot of sense and it's quite interesting. Because it's like the combination of these that really makes data governance work. I agree. I fully agree. And I see data governance is in many ways like documentation. So it can be very difficult to show the direct ROI of if we do this, we'll see this. So it often comes down to second order effects. And we touched on this briefly in the beginning. Back to it, which of these second order effects do you see or what second order effects do you see from having proper data governance in your marketing and really overall setup? Yeah. So I think I've touched on this a little bit, but it does save time overall. Having that common language in between units and individuals, I think, you know, it's a great way to get the data governance work together more effectively. So, you know, imagine a retail company's marketing team that's launching this multichannel campaign. And without the data governance piece in place, the marketing team might define customer lifetime value differently from the finance team, which will be leading to conflicting reports. So marketing could exclude shipping costs from their customer lifetime value, CLV calculations. So this misalignment really creates confusion and kind of hinders that collaboration and slowing down the campaign strategy overall. I'll give you just another example from the university where I'm working right now. The way that the university is defining alum is basically somebody that graduated from a Senate approved program. Whereas if you graduate or you just finished a course and you just finished literature 101, then that's all you've done. Then you're not considered an alum, right? And at the same time, the athletics department, it's considering anybody that participated as a team member of one of their varsity teams. So if they played, you know, forward in their football team or they were a swimmer or, you know, they played on the basketball team and so forth and so on, then for one season, they consider that individual an alum, regardless if they graduated from that Senate approved program or not. So that's an inconsistency. And initially when data governance was not there, this created a lot of back and forth and a lot of conversations, a lot of mistrust between, let's say, the athletics department and the faculty of arts, where they'll be like, listen, how come you did not include my marketing metrics on these individuals? I'm not seeing that in your report. What gives? What's happening there? And that's just like one example where this did not happen. Because obviously, you know, there's a lot of marketing materials being sent to alumni to make sure that they're engaged and, you know, they give back, they participate in events, they, you know, opportunities for them to become mentors and so on. So kind of keep them close to the community. So, but now, you know, there's a business philosophy in place that really defines an athletics alum different from just an alum as well. And that's clear to anybody that gets onboarded that's new to the organization and they understand the differences and why different reports might be different and really not using the same terminology. So that's kind of one thing. It saves a lot of time. Otherwise, there's a lot of meetings. Even when you're, you know, building a report, you have your IT team and let's say the marketing team and the marketing team is asking for the IT team, you know, can you please build me this report? And they are. Everything goes well. And at the end of it, a month down the line, the marketing team looks, now the final report without seeing that mock data that they had access to before. And they're saying, yeah, this is wrong. So the IT team goes back and they're going through their scripts and documentations and everything. And everything checks out. And they're also taking a look at the data and the data is okay too. And actually the difference was that they had a different understanding of what that customer lifetime value was supposed to be. They both came in with their own assumptions. Yeah, that's maybe one big one. But yeah, there's really so many others. Again, I think it doesn't just save you time. It doesn't just fix data issues. I think it really transforms how an organization operates, kind of sets the stage for sustained growth and innovation. And over time, it really creates a robust data-driven culture, which is equally important. It's quite funny because like the safe time aspect, the correct input aspect is not overlooked because it is in so many cases, but we've been used to a different market where it's been so, again, I think the pace that has been there and where we kind of second order effects sometimes are not even visible, but they are, as you said in the beginning, it's like the building, it's the foundation of the building that is kind of lost in that. So if we have the right foundation, we're actually starting to see the results. And our last webinar was with Qualcomm talking about these things as well. And they totally resonate what you're saying, which I find quite interesting, actually. They boiled it down to three parameters, which was technology, processes, and people, which is exactly what you're talking about here, right? And to be honest, I find it beautiful because it kind of puts the finger on, hey, it actually, the second, order effect is so wide and for so many people and has such a big effect and impact on the business in probably the three main areas that businesses, make businesses successful in this day and age, right? But talking about these topics, having a top five voice, top five influencer in the area and on the air with the AI aspect, I have to bring this into us because I find it quite interesting. Can you talk a bit about how data quality impacts when we talk about big data models and large language models and all these things that a lot of companies are pivoting into, which is a very hot trend, but I heard an analogy at a conference I went to where they said like every CEO want to sit in the bathtub, but no one is questioning the water going into the bathtub. That's the perfect segue. How do you see this, George? You know, by the way, one of the things I like about this rise of LLMs is that it's bringing a focus on data governance as well. And we're seeing a renewed interest in data governance. So overall, I mean, data is impacting the outputs of these LLMs, right? So you need high quality data. I think it's absolutely critical for this. And the problem is that these models tend to amplify any flaws, any biases, any inaccuracies in the data that they are trained on. So without that strong data governance, poor quality data can lead to subpar outputs and misinformed decisions and significant risks and so forth and so on. I'll give you just one example. There's this organization called the National Eating Disorders Association. And I forgot when it was last year, a couple of years ago, it ended up replacing its human counselors with Tessa. And Tessa is this Gen AI based chatbot. You know, there's a lot more bigger story than this and why that was done. But the outcome was the following. Within just one week, Tessa, the chatbot, was found providing advice that really contradicted established practices for managing weight disorders. And this failure was really likely due to the unreliable quality of the training data. Because from what I understood, they sourced most of this training data from the internet. And we know that the internet is, you know, rifled with great content, but also a lot of misleading information too. And the consequences were really severe. So the helpline was shut down within one week. Individuals that were seeking support were left without it. And obviously, the organization really suffered significant reputational damage. So I think this case really underscores the dangers of deploying AI LLMs without those rigorous checks and understanding its training sources and making sure that you're investing in the quality of that data that's doing this training. And yeah, there's so many other examples. Bloomberg also did this analysis of LLM AI generated images. That kind of highlighted the issue of bias quite vividly. So they were showing that when you're prompting to generate images of a CEO versus a fast food worker, the AI disproportionately depicted CEOs with lighter skin tones and the fast food workers with darker skin tones. And this bias kind of reveals, again, the critical need for vigilance in curating the training data and setting the ethical guidelines, right, AI usage. And I think in marketing, the stakes are equally high because poor data quality in AI systems and LLMs can really result in biased recommendations, you know, misdirected targeting, campaigns that could alienate segments of your audience, and ultimately wasting resources, damaging brand reputation. So it's very, very important. The WPP, which is the world's biggest advertising agency, you know, they're working with consumer goods companies like Nestle or Model S. They do the Oreo cookies, if you know them. And, you know, they use Gen AI in their advertising campaigns. And they were also underlying in this article by Reuters, really the importance of making sure you have that high quality that's feeding these LLMs. Unilever, again, their global VP of the go-to technology was telling Reuters as well that, and I'm paraphrasing here, okay, I don't quite remember the quote, but something like ensuring that these LLMs, when you type in certain terms, are coming back with an un-stereotyped view of the world is really, really, really critical. So, yeah, it's very important. It is. And actually working with some of these brands, we see this and we see the efforts that goes into the data quality and actually coming from the same things that you have been bringing up for, since we started this webinar, really. So it's quite interesting how we can impact data quality because now for the marathon sprint thing that we talked about, it's actually like we certainly have some of the end results of the marathon investments in this. So making it important because many, I think in marketing, especially, are looking at data quality as like, okay, but let's just pick up the quick wins here and then look at the rest. But really many of the quick wins are the first steps into the marathon. So like you shouldn't obviously sprint into a marathon, but you can make like some solid steps that will set your data quality to evolve step by step. Is this also something that you see with the organizations that you work with that they have an approach of like, we want to get to the end goal, but instead of understanding that sometimes there needs to be these steps, if you will. Yeah. Yeah. I think, I mean, a lot of them from what I'm seeing is they're trying to break it down into some actionable steps because tackling data governance overall can be daunting. And I think, you know, actually back to your, how we started the webinar, it's maybe what stops a lot of organizations to invest into it because they think it's so much that needs to be done that I'm very reticent of even starting. So yeah, you need to break that data governance program into small actionable steps. So for example, I recommend starting with a pilot project, if you will, for maybe a single marketing metric or a process like standardizing customer acquisition cost calculations or standardizing your digital marketing metrics or your customer contact data. All of these, I think would really bring some immediate improvements right away and not just within the marketing department, but organization wide. Then you can demonstrate those quick wins. So for example, you're reducing reporting errors, you're speeding up decision-making for specific campaigns, you're increasing reach overall. You know, imagine just standardizing that customer data, making sure that at least it's standard, you would, you know, save on costs. You would reach the right customers right away. If you know the customer acquisition cost calculations and that's standard across the different platforms, that you are marketing on in the different mediums, win-win. And then you can use these results to kind of build momentum and showcase the broader value of data governance. So you're building your case from within, from starting within one department and then kind of growing from within to spread organization wide. And yeah, kind of think of data governance as maybe planting seeds for long-term growth, but watering them with short-term wins to show that immediate results. So you always need to have to have that balance. Yes, you have the whole strategy and where you want to want to get to, but you need to keep on showing the wins and how does that tie into the unit goals, to the organization goals, even to the individual goals of the people that are being impacted by this change. Data governance brings in a change to the organization. Yeah, absolutely. And it makes a lot of sense. Kind of like you need to be a bit bold to do these things because you need to be convinced about data governance, which I think is like one of the really great things that, that, that Lysol data is doing, because like, how do we understand this in a language that we all speak? Like that, that's, that's, that's definitely a big challenge that, that we see in the data governance part of this. And I, and I think you always kind of need to highlight the cost of inaction. Hey, if you're, we're not doing this, this is what, what's outlining the fine, what, why it was given to them. One of them was a lack of a data governance program. Imagine like not having it, it cost them $400 million. I mean, that's a, that you can buy a lot of, that's money, a lot of stuff with a lot of Oreos. Yeah. Yeah. Okay. Yeah. But that's, and I love seeing these things coming out. And I guess that you're seeing a lot of these as well. Pandora just recently released their, their, their yearly report showing that they like really outperform entire industry, their entire industry in growth from actually focusing on digital and how they collect data on these. And their article like stated that, hey, data governance is the way that we are moving forward on this, which I think is, is a really positive trend for everyone interested in data governance and working within data governance that, that suddenly the world is seeing the superheroes that has always been doing this and preaching this. And, and doing all these things. Is this something that you see in, in, in Canada and North America as well, that, that these stories are starting to, to appear more and more? Definitely. And especially those companies that are aiming to be data driven or data informed, then they're looking at these stories for inspirations and that, that, that helps. Yeah. Yeah. And it's really interesting tying it together with, with the business growth with, with the existing technology out there it's like, you know, we couldn't go back. And the understanding of technology is like a super good lesson to learn. the audience around uh you have a big following that so i guess that you are used to getting all sorts of questions today um it might be a bit more marketing driven but uh we collected questions from the audience here today which is awesome and i see questions coming in here so keep them coming um and while we do that let me just uh start with a question that was brought in from our web page um when we promoted um that we would have you on which is actually from uh the analytics team side saying how do you get something that's important but long-term prioritized when there's normally when there are normally so many short-term important tasks in analytics team so kind of talking about long-term short-term but now in terms of the task list that's that's coming up so how do you prioritize data governance when we need to cover so many day-to-day tasks uh when the business shouts for it for different things yeah i mean you know uh it really depends on on your context and and your audience i think it you kind of need to do your homework and understand your audience and see who are you pitching this to do you who do you need funding from or who do you need to support you to to get that um that resources in place and to let the teams really focus on this while other things and other fires need to be put out so um it sounds maybe a bit odd but when you're doing that you're often time speaking to one individual or maybe a group of individuals so you kind of need to understand well what drives them what how how can you get that point across to them are they a number persons are they a story person you know should you pitch in a story like uh the one from uh cd bank or um pandora or the uh um you know any other companies that we've seen that they've done something with it or they were affected by a lack of data governance and maybe that's what drives the message home or do you want to show some numbers and say listen this is how much time we're wasting or look at how much time it's taking us to put this together or to um make some really quick wins and decisions on our marketing efforts or look how much money we've invested in this campaign and it did not get us anywhere because we were basing all this on god feelings and maybe previous experiences but not on data so and sometimes it might be a mix of this to kind of drive the message across and really convince them but in the end i think you're speaking to an individual and you need to tie it into the what's in it for me and it's good to understand what's their pain point that they're trying what's what's hurting them right now and then to tie in how data governance short term or long term is able to get rid of that issue that they're facing do you see tying into so from the analytics team when people come when a manager comes say hey i need the numbers and this uh do you think this ties into obviously understanding that the the context that the person is coming from but saying hey actually if we look at it like this we would able to be able to accumulate data um would would kind of like have like short-term things you need to do every day and at the same time work at these these more long-term uh instances exactly and you know especially when people are coming back from conferences and they're seeing all these inspiring things that our companies are doing they're saying hey how come we don't have this and you're like well this is one of the big reasons why let's invest in the foundation first you're you're telling me that you want ai but we don't have the foundation laid out initially so and you know that's a great segue for you to kind of pitch in again maybe the the rationale why you need to invest in some uh some of these longer term value driven programs such as data governance it's absolutely spot on that's yeah i i completely agree all right i'm looking to the audience questions here uh so before we end let's uh bring a few on here we have one from um sebastian asking in the context of marketing data what key characteristics define ai day ready data and what best practices would you recommend for organizations to achieve and maintain this level of data readiness you have on the web that the photographer never gave its permission to be used like that so anyways that's on the ethical side so it needs to be ethically sourced and it needs to be as much as possible without any bias and of high quality so once you have these in place i think you can say yeah it's it's ai ready but at the same time it needs to be described and understood through your own assumptions you need to clarify those assumptions and to understand that then the ai will take on these assumptions and work with that so once you have this i think you can you can you can mention hey it's it's ai ready which of course it's even more difficult with unstructured data uh than it is with with structured data that makes a little sense sebastian i hope this answered your question um george you have time for a final question here yeah let's do it perfect so um we have a question from tobias saying there is a lot of footwork in getting a culture of data informed decisions have you seen ways uh that helped to achieve this which is feeding very much into to what we talked about also with with the people aspect of this right yeah really quickly i think there are actually seven things that i think you need to do so one you need to get that leadership sponsorship so leadership must be consistently championing that data driven decisions and demonstrating their commitment through you know action right i mean when executives are openly referencing data and their strategies and showing how insights kind of guide their decisions and really demand the same from their teams it really sets that powerful powerful example and strong leadership also means you know allocating the resources and prioritizing data governance initiatives and removing these obstacles so you know approving um your your your program uh you know for example ensuring funding for user-friendly analytical uh tools you know solutions like Accutics uh and training programs overall really signal the importance of data-driven practices to to the rest of the organization second you need to invest in that education and storytelling i mean you know offer regular hands-on training that kind of connects the data insights to business outcomes uh and there's a lot of examples there and again brent dykes is really great to to provide you with samples on that um embed those data champions so assign data champions in kind of each department or identify them they're usually there because they then they help to foster that data-driven mindset at the ground level and you need both you need the top support and you need the ground level to to push and to demonstrate this is working and they also kind of bridge the gap between leadership vision and day-to-day operations celebrate the wins so publicly kind of celebrate successes that are achieved through data different decisions make it known that hey we this is what we like and we encourage it uh create that shared language again breaking down those silos is critical and i think this is what data governance does very well um incentivize those adoptions so reward teams and individuals who are effectively using data to achieve their goals and again that can be done through recognition programs maybe bonuses a note in the newsletter uh you know maybe even career growth opportunities and the last one is again provide those accessible tools so equip teams with intuitive tools that kind of make it easier to access to interpret to to act on data so yeah just to kind of sum it up i think data informed cultures or data driven cultures get formed when leadership sets the vision the resources are being allocated to support it and then the employees feel empowered to act on data wow that was amazing george that's uh that's summing it all up um so so perfectly um so thank you for for sharing your insights today um it's been a pleasure having you on i uh hope that we can have many more of these conversations um and i really really love your approach of the whole data governance into the marketing leg as well uh i think a lot of people are finding this very inspirational and and um i know they are following your podcast and your youtube show and and lights on data in general so thank you so much for coming on today and sharing your insights last question really quick question i leave you what is the best thing about working with data governance every day you're fixing things i feel like you're always improving something so it you feel in a sense of accomplishment right away perfect answer george thank you so much for being on the data uh leader series it was a pleasure having you here and i i hope we can can bring you in another time thank you i really appreciate it to everyone watching out there um i wish you a great night if you are in the same locations as we are and a great day if you are in the same location as george is so everyone have a great one and we'll see you for the next webinar which will be the third week of december where we will talk much more about um the data heroes of 2024. so keep an eye on the site and tune in for the next webinar and have a great day out there goodbye you