 Hi everyone and thank you for joining us today for our webinar on Navigating Modern Data Culture. We're looking to explore the dynamics of data strategy and how companies can make sure that the title Data-Driven is more than just an empty password. Making data a key part of everyone's job requires a concerted effort from all level of the organization, from the CEO to the frontline employees. I'm Alessandro Davoglio, I'm VP Sales here at IGNUS and I'm really excited to introduce the amazing panel that we have with us today for our discussion. So we are delighted to be joined by Jordan Moro, founder and owner of Bode Data, Godfather of Data Literacy, Catherine King, Global Head of Brand and newly appointed editor-in-chief of the Driven by Data magazine at Orbition, Ed Santana, Head of Products at Koyo, and last but not least, Pete Williams, Director of Data at Penguin Random House UK. Thank you guys for joining me today for today's discussion. So our agenda today is going to include a talk from Jordan to kick off the conversation, followed by a panel discussion on how great data culture can lead to great things at any organization. So without any further delay, Jordan, the floor is yours. Wonderful. Thank you so much. Let me pull up my presentation. Please let me know if you aren't seeing this and we'll get going right into it because I want to make sure I provide plenty of time for the panel to be able to answer questions and you're not just hearing from me. I will make one clarifying note. I did not give myself the nickname Godfather of Data Literacy. I have two nicknames out in the industry. The one that I really do like is Chief Nerd Officer, and I am more than happy to have that nickname. The Godfather of Data Literacy, a little so-so on. I remember being in a meeting and someone said to me, you know what they call you, right? And I said no. And she said the Godfather of Data Literacy. And I'm like, well, I'm not sure where I feel that one resides. The reason I think I have that nickname is I helped to pioneer and invent the entire space. And of that space of data literacy, there is a key component to the holistic approach to data strategy succeeding. And that's the topic of today's conversation. And that is data culture. And how do you empower this? What does it even mean to have a data culture? And so to begin just going for about 10 to 15 minutes here before we do the panel, let me talk about this evolving world of data that we live in. And I'll go through maybe a couple of these questions. But the reality is, we are in the birthday month, if you will, of chat GPT at launch in November of last year. It's not even a year old. But this world of data and analytics organizations have been trying to capitalize on it for a long time. But the number one roadblock to data and analytics success is people or the culture of the organization. And a big part of that is, is fear being overwhelmed or it's just not a person's background. So you look at these questions that sit on your screen. What is my role in the world of data and analytics? Do I have a background that can use data right as robot or our robots going to take my job. And the way I like to look at this from a person, people or culture perspective is that every single person has a seat at the data and analytics table. You're not required to be a data scientist, a data engineer, machine learning engineer, any of those things. What we want to grow, of course, is data literacy and your comfort and confidence in doing that. But in order for that to occur, people need to understand the purpose of data. Like the culture needs to buy in and understand what is it that we are going to try and do to utilize data. Because we can source data. Last year, there was a prediction prior to chat GPT coming out. That said, by the year 2025, the annual data sphere. And I think what they're getting at is all the data in the world will reach 180 plus zettabytes worth of data. And zettabytes represents 21 zeros after the number 180. But how many organizations are truly capitalizing on all that data? How was that investment going? Well, we got to remember data is just data. Data just sits there. It needs something to bring it to life. And that something is analytics. Now, analytics is driven by technology or by people. And this is where that culture comes into play that if the culture and the people are not confident and comfortable in utilizing analytical work and data work, are we going to get anything out of it? Right. If we were to think of an organization of 10,000 employees, maybe 100 to 200 of those employees are true data professionals by trade or title. That's it. So now you're looking at 9800 or 9900 who might be asked to utilize data. And if they're not confident with it, that culture could really quick shut down on utilizing data. Now, analytics and either the technology or the person is trying to bring us insight because then once we have information and insight, we can make a decision. And those decisions are there to empower the organization's business strategy. That's data's purpose right is to help an organization succeed from its business strategic perspective, and we need to empower everybody to do that. Now, to do that and to empower the culture we do look at data literacy and the four characteristics of data literacy are this ability to read, work with, analyze and communicate with data. So when we think about the culture of the organization and what it means to have a data driven culture or data informed culture, what we want is for those 9800 or 9900 to feel confident and comfort holistically utilizing data to do their jobs better. Can they read it? Can they work with it? What about the analytics? Do we know how to analyze it? And do we have the ability to communicate effectively? Now through this notice what term is not on there. AI is not on there. Data science is not in there. Data engineering is not on there. I don't need everybody to be that sort of technical professional. Neither does your organization. But we do need to know how to interact with AI. We need to know how to read results. We need to know how to interpret. We need to know how to communicate. And so when we help a culture succeed in those ways, we can drive a data driven culture where we are utilizing data on a regular basis to transform, inform and help us make decisions. Now, my key pillars of a data culture and I'm excited for the panel to speak more on what a data culture is, but these are six pillars I teach around data culture. Data fluency. You need people to be able to speak the language of data. You don't want people staring at you like a deer in headlights because we're using too much technical terminology. We're using coding language. All these things when the majority of people don't know that we need iteration data and analytics is not this set it and leave it alone thing where you hope I put a model in place. I'm going to run perfectly for three years. How do we put models in place in January and 2020, and just left them well what happened in March or around that time. A lot of places around the world shut down with the COVID pandemic, you needed to iterate right so iteration this idea that we will reevaluate and continue to do data work. We need the DNA of data to weave throughout the organization. We don't want to change it. We need to evolve it. And so can we weave the DNA of data use throughout it. Another part of the culture that we want to see succeed is having a community. Is there an area where all those that are non data professionals can go to ask questions to learn to be trained to fill in the gaps to assess their knowledge. What are we doing from a community perspective to help this culture thrive. Number six is a big one for me. I don't like the phrase fail fast. I like the phrase learn fast. Nelson Mandela is a hero mind in fact on the bookshelf behind me. I've got I don't know how many books around him on him or whatever had a quote that went something like I never lose. I either win or learn. That's dated analytics. If we work on something in dated analytics and it doesn't work. It's not that it failed. What can we learn from it so that we can pivot and drive analytics and insight better. And then finally we take a look at my last pillar on there. It's data skepticism. I want to create cultures where people question everything. It doesn't mean that what they're questioning is wrong. What it means is we're just having healthy skepticism not cynicism. We need to be able to question things if data comes out. You're not sure about it. Question it. If things are popping here and there. You're not sure about it. Question it and we want that so that we can iterate so we can use data fluency so that DNA is going through it. We have the community to discuss and we're learning quickly. So these are just six pillars. There's more things that we could do. There's things that I'm missing. I'm sure. But if you're thinking about a data culture, I would say having these six pillars helps to empower individuals and organizations to succeed in data and analytics. There is no easy button. You can't just buy one solution and think that's going to solve it all. It's people. People comes up a lot when it comes to data and analytical work from the investment people are willing to make to the skills they possess. The final thing that I'll mention here is ensure that your culture is thriving with the three C's of data literacy. Curiosity, creativity and critical thinking. If the culture is having these things in it, curiosity, asking more questions. I think children are wonderful examples of good data literacy and data culture because they're always figuring things out. I've got five kids. I get a lot of questions. They're figuring things out. They want to learn and they want to know businesses that are not curious are going to impede their culture. Number two, creativity. If not, everyone needs to be a data scientist and that's not the direction we're going. We have to have creativity built in and critical thinking. We need people to think deeper and better on data. I'm reading a book right now called stolen focus by Johan Hari. I also recommend a book out there called deep work by Cal Newport. The book I'm reading right now says teenagers and I might mess up my metrics a little have an attention span of 65 seconds. Office workers, three minutes. If we're trying to truly analyze data and utilize it to inform decisions, we need to push aside distraction and to think critically on the information. So with that, I'll say connect with me. Thank you so much for this chance to speak for just a few minutes on these things. There's my company and my books. So with that, I will say thank you. I'll turn it back to Alessandro so that he can open up and have this wonderful panel answering some good questions for you. Jordan, thank you. Thank you very much for this great presentation. I'm sure that we will build a lot of those points on data culture and especially I think you mentioned one important thing that sometimes I think all of us, especially when we work in with technology, we forget, which is people. So I was really amazed about how you really put the people at the center of all of the conversation. So building upon of that. Pete, I'm going to, I'm going to start with you. Jordan mentioned the importance of community perspective. And now I focus also on non technical roles specifically. It's important. So how do you think is important that point in creating a data literate workforce? I think community is key. Actually, in the past, there's been lots of models around centers of excellence and places where people go to receive permission or insights and then they go away and use those. But I think increasingly over time, I've found that if you're going to try and generate the sort of insights and the sort of culture that we're describing here where everybody can generate insights, you're not receiving and then using insights. Then you have to connect people together and there's always going to be data that people don't know about that's locked in silos or in other people's databases that can be shared and communicated. There's always going to be ways of achieving insight that people can share between them different models, different approaches and bits of unique information that just don't percolate through the entire organization. And there's going to be different ways of distributing that and getting it in front of the right people using the right sort of processes inside your departmental area to make sure that everybody from top to bottom is understanding what you're trying to do. And I think, you know, that top to bottom piece within a function has to go sideways. And for me, this is the genius of data. It's the horizontal joint across every organization. And that sort of sideways movement just me do it with my hands. It just shows that we're talking about a spread of community. We're talking about a widespread adoption across a number of people departments. So community for me is absolutely vital. If you're going to spread what we describe me around the organization and take benefits from it. That's great. And to build again upon on what you just mentioned, I'm going to move to cut. You definitely work with a lot of industries and a lot of people in several departments across the UK, building upon what Pete just said. Have you seen any common denominators when it comes to gaps in that kind of data culture strategy that organization are trying to achieve? Yeah. And I think, well, I think Pete's absolutely on the money with saying that you can't just live in this in this data silo. And I know there's loads of data leaders out there that really hates this term of data and the business, separating the two because data people are the business with, you know, we're part of the same thing. And I think language is so powerful when we talk about these things of the separation that can occur. And actually, those that are further ahead in this naturally tend to be the more digitally native. They tend to have had data to begin with. So it's almost an assumption that data is there and embraced. Now, that's not me saying that there doesn't need to be some sort of central team set up for governance, privacy, security, all of that sort of stuff. Someone does need to own that. But when it comes to insights, if you are the more digitally native, then generally speaking, that is held within multiple departments. And you mentioned there about the sectors and industries that I get the privilege of seeing. And it does tend to be those who are dealing with a lot of legacy, and they're dealing with a lot of business that's been going on for hundreds of years, you know, that way before data, way before dashboards. And there is that culture of, well, we've been successful for 200, 300 years. And now you're coming along and telling me I need to start thinking about X, Y and Z. And again, that's where the people aspect comes across. Because if you're not digitally native, if you've not got the assumption of data in the form that we see it, it can be that you've got to go and win the hearts of minds of people that have perhaps not had to, you know, leverage and use it in the same way. Absolutely. You gave me a good assist for my next question about the technology that we're all using in our day to day kind of experiences. But moving to you, Ed, and again, kind of going further on this specific topic, you as well work a lot on different projects and you're involved in a lot of AI initiatives. So we've been just mentioned about gaps and communities, but especially with the push and the hype that we see a lot on AI right now. How do you see this gap going further? Yeah, it's a really interesting one, isn't it? So AI at the end of the day is another tool, right? But as others have been saying it's all about the people around it. But I think the key thing about generative AI in particular is just that it has empowered a lot of people with access to it. Interesting that Jordan mentioned that, you know, it is Chattipiti's birthday. I'm sure that if you ask Chattipiti to create a poem about it, it will create it to celebrate its own birthday. But that's the key, right? So a lot of organizations, what we can see is their employees are so excited about it that they are using it themselves, right, directly on the Internet to somehow help them do their jobs. The challenge then becomes how do you bring confidential data from your organization into that environment? Because the one thing you don't want to do is just copy and paste your confidential data into Chattipiti. That's in the public domain. So really the challenge and the things I've been talking about with businesses is how do you bring that in a way that is organized and controlled without stopping that excitement? Because the excitement is actually, I think, a good thing. Absolutely. And again, you gave me again a good breach to my next question, which is still about the technology. We mentioned Chattipiti, we mentioned other tools. Something that I see as well that I wanted to discuss with you guys is I often have conversation with multiple types of stakeholders from, let's say, technical by perspective or business by perspective as well. And usually these two groups have sometimes, no usually, but sometimes have completely different views on what they're actually looking for. So again, this is a question for all of you since you have a lot of experience in conversation with these different types of stakeholders. How do you make sure that the need at the end of the day that we want to satisfy is connected between these two type, these two big branches of stakeholders? If I could, if I could jump in, because I think Ed touched on something so valuable and I really want to draw it out, which is how people have been using Chattipiti who've never really embraced data before and never thought to. And it's that element of self discovery. They've gone, how can I use this tool to make my life easier? And it could be personal, it could be professional, but they've been in the seat of control. And I think historically data folk have tried to speak to business folk and say you should use data in this way and the business in whatever form, whether it's marketing or finance, etc. Have maybe not had the language or understanding of what data can do to be able to say, well, actually, this would be better. Whereas what we're seeing with Chattipiti and Gen AI is actually the individual user having a lot of control and autonomy to be able to say, well, looking at this, what this is providing. I think it's going to be really useful in this way and it's allowed data and business leaders to communicate in such a close way. Because now they have a jumping off point because you can say, well, you know how it does this. Well, now if you use this tool, this is going to keep your data safe. And I think this is where culturally it's been really interesting to see how companies have responded to this technology. And I know there's been some very big brands that have said absolutely not no one's to use it. It's blocked on your your browsers. You can't use it. And there's been the other extreme where people are like absolutely go nuts with it. Just don't put anything overly sensitive into into the the Chattipiti technology. So I just wanted to draw that out from what Ed said, because I think there's a real interesting element of self control. And it's allowing people who perhaps have never been able to communicate effectively before to have a really strong discussion. I'm muted here. Sorry to jump in on you. I've unmuted because I'm really get shot down by everybody, especially Jordan. But when I look at the four pillars of data literacy, we talked about first, read, work with, analyze, communicate from the demonstrations I've seen and from like articles and conversations. I am absolutely convinced that those first three could undergo a radical alteration in the near future based on how you can now interact with data. Because a lot of our data literacy training is get confident with charts and graphs, move away from grid of numbers, you know, and to start talking about how you build a presentation and stuff like that. And now we're actually saying just just talk to your data, you know, we're doing the Starship Enterprise thing. And for me, the ability to read, well, that goes away, because you're talking, I mean, I'm not 100%. But if you go with the sort of mindset, really, I'm having a conversation with the main computer and saying, is this right? Work with and kind of say, I'm going to take this and put it over there. Or if I was to do something X, what would be the return Y? And in the past, I've had to build some sort of scenario model for that, or I've had to build some sort of, you know, intricate architecture or ask a question on that from somebody in the, in the center of excellence I talked about earlier. And the one that's remaining is communicate. So how do you take the results of your first three, which have been transformed in how you achieve them and pass that message to somebody else to activate. And I think, you know, it's kind of, it's not overnight, but I think the first three are up for grabs in this current world that we're living in. Discuss. Well, Pete, you're talking to the guy who helped invent and pioneer the film and I agree with you, I'm not going to contradict you one bit. It becomes data and AI literacy, I will say that there's, I think the three transform and still exist, right, because you have to be able to read, you have to communicate and prompt the AI well, but you have to be able to read the results and work with them. And so it becomes no longer, I'm with you. I do not like data literacy programs that focus on storytelling or visualization because that is not a holistic approach and the majority of people don't have to do that in the way that they teach those things. It is been more of the people side and how you communicate and how you interact and how do you do things. And I think AI is an augmentation of that ability, not a replacement. And it becomes data and AI literacy. How do I read what the AI gave me? How do I work with it? Do I need to go back to it because I'm analyze it and re-communicate? And do I then know how to utilize that to make a decision? And the going back to a question, Alessandro, I think it was two ago where you're talking about how do we bridge these? Where organizations, I think need to take a step back is data might not be ready for AI. So please don't jump at it too quickly. But number two, they ask, how can we use this? They should already know because a data and AI strategy should be your business strategy. What are you trying to achieve as a business and use these tools to get you there? And Pete, you nailed it. It is no longer... I have to use my training to learn how to do Tableau or click or power BI. I need to learn how to interact with the tools that are going to do that for me. And do I manipulate it here? Do I iterate it here? And then I need to make a decision and not be overwhelmed by what's out there. But empower yourself to use what's out there. I think you're spot on. I think these tools, based on what has been said so far, should be a part that helped the culture thrive, not be something to fear. But it is incumbent on the people at that point to stay on top as best they can from a skill perspective so they utilize it correctly. Yeah. And maybe the communicate aspect comes before you even analyze anything, which is, okay, so what is it we want to achieve as a business? What is our strategy? What is the common glossary of terminology? What are the metrics we want to focus on? And then we can start thinking about the analytics because, to Jordan's point, it's all about the business outcomes at the end of the day, right? We need to be very concerned when you think about how we implement some AI here to fix all my problems. No, no, what's the business goals first? Okay, now we're talking. Well, and if I can have real quick on that is what may be a hindrance to the culture is when the people hold on to what they have instead of evolved what the technology provides. And what I mean by that is if you're an engineer whose specialty has been coding in XYZ for years, and you're just holding on to it because you're fearful where's my job security, that can impede the culture big time, right? Because GPT and Generative AI can help you with your code. But if you're like, oh my gosh, what's going to happen? So it's this whole allow the business strategy to guide you and you need to evolve with it. You need the community to do that. You need people to do that. You need all that. But when a company holds on to the past, then I'm sorry, you might be the company that gets superseded pretty quick. I think it is important though to acknowledge that those fears are incredibly valid. And I think we just have to look at previous industrial revolutions to know that people will be losing their jobs. And it's, you know, I know people, they will lose their jobs in the traditional sense of what they are now. Will they, you know, they're not going to just land up on the unemployment line, they will be changed and evolved. And I think this is where data leaders that I see are getting this so right are the ones that are already upskilling, developing their talent and showing you where the road is going to go forward rather than just go, no, no, you're fine. Whilst in the background, working on tools that are going to replace that role is actually being very proactive in the saying, you know, what, what your day job is right now is going to change. But here is the development plan to make sure that you stay in this company because you have so much knowledge in your brain, you understand how our business works. And these are all valuable things that you can't just replace with a tool, you know, that human essence and connection is still so important. But I think we mustn't just brush this fear of job security and humanness under the rug. We have to address it and understand that these are real human beings with feelings and that's going to impact business because if everyone feels insecure and they leave, then guess what? Your strategy is still not going to go ahead either. So I think having that talent development and understanding that journey and part of that is looking in a crystal ball as well, right? We weren't talking about chatGVT this time last year. So goodness me, if we meet in 12 months time, we don't know what we're going to be talking about then. So we have to be as proactive with the information we have in front of us, I think. Absolutely. And all great points, guys. So thank you very much for making such an interactive discussion. I have a question since we talked about a lot about the development of this strong data culture. But in your experiences, what are the main roadblocks that you've seen happening when you want to promote a kind of data culture? We talked about the tools we talked about accessibility. We talked about people. What would you think is the main challenge to start this journey? So sorry to leap in, since my colleagues are not doing so. I think one of the things around creating this is the culture in an organization is not written down. You can go to a booklet and say, what's the culture of this company? But the culture of the company is embedded in how people interact and behave with each other and what they do on a day-to-day basis. And I've been, I'm going to say professional, I've had a job for 30-odd years now. And I've been through lots of culture change programs where the ultimate outcome is pretty much what was there before. But it's been two years of angst and hanging in between because what people are doing is what really counts, what people see in a day-to-basis, how they're treated, how that comes down through the chain. So I think the roadblocks are, it's around making sure that top to bottom behaviors are being modeled. The things that you want to see are what you see on a day-to-day basis because you pick up on those signals. You know, if you hear one thing and you see another, you tend to do the other because that's what's really expected of you, not what's written down. So in the roadblocks to getting where we want to be in terms of the sort of literate workforce that we're describing, are around getting around people who don't think that measurement is important, that don't think the performance can be harnessed in some way and made visible. And, you know, we've talked about various ways that data and performance is visible now compared to how it might be in the future. But it doesn't change the fact that you have to start measuring things. And even if you're massively entrepreneurial and you're all on gut feel, the result of every decision might be data you've lodged in your brain, as opposed to data you're getting out of a Power BI or a Tableau report. But it's still based on data that you've assembled yourself. And ultimately, in every organization, you have to prove that what you set out to do has actually added to the bottom line. Because if you don't prove it and you carry on doing it, you won't be there for very long, either as an individual or as a company. Or if you have proven that your gut feel or your entrepreneurial instinct was correct, then you need to double down on it and optimize that opportunity. So I think that the biggest thing that gets in the way is not setting out to measure stuff and not modeling that behavior forward. It's like everybody at the bottom has to print off a report. Everybody at the bottom has to look at a dashboard. But at the top, I just received messaging about that. And I never actually asked for the numbers or anything like that. So I'm probably going to distract and go all over the place here. So I'm going to stop right there. I would just say these things are embedded in behavior. They're not ran down on paper. And you have to demonstrate and measure and reward and progress and decision make by the evidence that you're presented with. And it's only when you're doing all that that the rest of what we're talking about becomes relevant to the day-to-day operation. Man, to continue on that, Alessandro, one of the things Pete's hitting on spot on is do people truly have an understanding of this? If they don't, you're not going to succeed, especially if leaders do not understand it. One of the things to Pete's point, when you drive a change management program for two years, if people don't understand the why, good luck. And I would argue that in the data space, what's happened a lot of times is, number one, they don't understand the why, but they know they want it or they know they need it. And then they force it on people. And when you force this on people, good luck when 99% of people, 90 to 99% of us are not data professionals. But yet, here's the company saying, we just spent X millions. You now have to do this. How many of us want an email that you have mandatory training? How many people like that email? If you force data, tools, technology on people without like, for me, I teach have a really strong communication plan. Ed, you were talking about this even before analysis work. But even before you drive change management with data, how are you communicating this out? Do people buy in? Do they understand the why? Do not force this. If you force it, give me a year to two years. I'll sell you a different product and I'll get a nice commission and then boom, we'll just keep going and going and that's what a sales rep can thrive on. Versus take that step back and say, do people understand why we're doing it? Do all this correctly? Then maybe you can thrive with your culture, but quit forcing it because that's like, I can't, I'm an ultramarathon runner. I love it. I haven't run one in a since last year. And it's like, if I said I'm forcing everyone on this panel, you're running this with me next year. Yeah, I'm not going to get buy in. That's not going to happen. So it's same thing with data. Yeah. And that's a great analogy, Jordan, because I was just thinking, you know, I'm a big fan of small, iterative, simple projects that make up a two-year program possibly. But if you start from one department, one or two use cases that you involved people to actually, you know, come up with in a hackathon or whatever you prefer. Then you start identifying your enthusiasts that would then say, this works guys, you should implement it in your side as well. And then to your analogy, you don't want a marathon from day one. You do your, you do your couch to 5k. Then you do a 10k and then you progress. Right. So I don't understand why businesses want to just go and make a big transformation and force it into everybody. If you do it iteratively and to Jordan's point earlier, learn fast. That tends to work much, much better. Yeah. And I think building or what Jordan was saying there about communication, it's so key. And I think one of the main roadblocks that I see people run into time and time again is not developing their own soft skills of really listening and understanding who they're talking to. And I think it's just good marketing at the end of the day. The more personalized you are in your emails and communications, the better results you see, right? Because we don't just want a broad brush, you know, stickle kind of message. We want something that's tailored to us and no organization. I think you can, you'd struggle to generalize any organization as a whole of being a certain data culture because there's going to be silos and pockets of excellence where they're running full speed with data. And then there's going to be other areas that it's like data who. So you have to understand who you're talking to and how you're bringing them on to this journey because everyone's going to be at a very, very different stage. And that's the fun of being a data leader, right? As you get to juggle that and it's really, really difficult, but you have to go in and listen and understand the challenges and also how it's already being used so that you can help them further on that journey. Because some of them it will be a case of just continuing to upskill and continuing to work with others. You really have to go back to that basics. But as Jordan says, it's uniting everyone around that business. Why, regardless of where you are on that journey, it's saying this is why we're going, this is where we're going rather and why we're doing it. And I'm going to meet you where you are on that journey to help you achieve that goal for you and also linking it back to people's KPIs, right? People care about themselves, funnily enough. So if you link it to what matters to them and how they're going to achieve their Christmas bonus, you're going to get far more, more buy-in and results through that. I think everything that we've just said between the three of us boils down to the fact that you can't just have Jordan's book on data literacy. You need John Gotta's book on the principles of change because what we've said is create the compelling vision. You know, understand the burning platform, get people to buy into that. Ed, you were saying create those short-term wins because people struggle to see two years ahead, but they can really see next week. So understanding what those things are, they'll get them on the journey. Understand the WIFM, you know, what's in it for me and channel your communication to all those. And I think this is one of the many reasons why whenever I talk about the role of the chief data officer, you know, in my sort of three diagram, then module, one of them is always change management because ultimately you've got to be able to sell this to your organization. And then again, again to Jordan's point, you can't just say you're going to do this. You have to get people to say, I need to do that. And the answer is X and I want to be part of that journey because then, you know, you're on the way. As soon as you're trying to say to people, you have to do this, then you're facing obstruction. So change management, the ability to lead change and transformation is fundamental to being a data leader and essentially getting data literacy into your organization. Well, amen. And yes, as much as I want everyone to buy my book, you need more than just my book. I don't disagree. Like this and this is something I've been speaking of recently is the forgotten piece of data and in fairness to data professionals. It's not what they trained to do, right? They trained to be data professionals. But if you can't lead with change management and get buy-in, good luck. And I wish you well on your journey, but I'm with you. You need to buy multiple books. You need to figure out how to really drive this to succeed. This is absolutely great, guys. And again, thank you so much. I think I'm learning a lot from this conversation and we're getting towards the end of our webinar. So we discussed about the importance of the why, so the what's in it for me. We discussed about the what. So my last question would be around the how. So technology-wise, there is a big debate, especially now with the wave and the hype of generative AI. We discussed at the beginning about build versus buy and organization. So this is a huge debate. We're not going to cover it all today, but I would like to get points from each one of you on what are your thoughts about building applications internally, these kind of applications, the risks about building this kind of application, the preparation that you need versus buying and therefore being able to evaluate solutions in the right way. Ed, I'll start from you. Yeah, that's an interesting one. First off, it depends on what we mean by build. Do you want to build your own large language model from scratch? Most businesses absolutely don't need that. And that's actually the beauty of what we've seen with GPTs and similar, which is you don't need to put all of those data scientists together with a lot of data to train everything. It's ready. You can apply it. And the beauty of it is how to apply it to your own use cases, right? I would say that you probably want to find products and solutions that exist that you can bring in and then focus on integration. This would be my suggestion, right? Integration to your business processes, to your business outcomes, to your business data. Because often you have these technologies from outside and they are great in isolation, but you still have your spreadsheets, which will never go. You still have your CRM system. You have all these other things, so bring them all together. And I would also say, so talking about reading books, bringing external expertise. There is no shame in that either, right? And external expertise to even have that communication aspect of gathering the right people from across the businesses and running your workshops and ideations and getting some planning together, all the way through to bringing some expertise for the actual hands-on. I think the best ones tend to be ones that will come in with their external expertise, but still train your company up to retain some of that intellectual property and they will then help you maintain. Because to Jordan's point earlier as well, it's not a hey, it's on, it's working, I'm off. You have to maintain, you have to continue improving it. So my suggestion is don't build it from scratch. In most cases you don't have to. You can buy from outside and then focus on integrating and getting your business processes done. What do the rest of the panel think? I'll jump in and say, amen. You don't need to reinvent the wheel. The wheel was invented before. But what I would say is have a very honest, transparent conversation where your company sits, right? And what I mean by that is don't get caught up by some sales vendor who made some tool look really sexy. They're probably using really nice data to make that tool look as good as it does. Evaluate a few tools. Have honest conversations. Bring in outside voices that can be honest with it and take a look at it, but take that step back and evaluate where you are. It could be that you're not ready for a large language model and that's totally fine. If your BI is not working well, your AI probably is not going to be some thriving thing in your organization. So just take that step back, evaluate, look at multiple vendors, ask the vendors a very specific question around, hey, make this work on my data. I want to see it work just as well. Because if they're using perfect data, it's going to look amazing. Then you're going to bring it in-house and be like, man, that did not work. But that's why I evaluate, bring in the expertise. Ed said it well. Don't reinvent the wheel. Right there are tools out there. But have that honest conversation with what your strategy is, what your data looks like. You might not be ready for AI and that's okay. But small things, iterative steps like Ed was saying. Yeah, I think this is where the power of speaking to fellow data leaders is so key. There's so many conferences and communities in our space. And something that I have really enjoyed over the last five years of my career is how candid people will be. If you are really liking the sound of a particular vendor, generally speaking, on their website is all their customers of who they've worked for. Go and meet with that data leader and be like, hey, I'm also thinking about using X, what's been your opinion of them. And I think just relying on the community side to it is actually a really strong way. And generally speaking, fellow data leaders don't have as much of an agenda as what you were saying there, Jordan, behind. If you've got someone who's obviously trying to sell you something, they are going to show you the best version of that tool and capability. And of course it can do that, but very rarely are we in a situation where we can deploy it in such a way. Definitely my advice on build versus buy is go and speak with your fellow data leaders, go and access your community and work out what others are doing in that space. And if I'm going last, my general philosophy, having been a coder and a developer in my life, don't try and solve problems somebody else has already solved. I mean, if you've got, if you've got a limited budget, limited time, people in the organization are screaming you for solutions and things. And why sit down and build, I've built this amazing connector between these two databases in Python. It's like, yeah, okay, but this guy's already got one, and I can do that, put my credentials in and everything's flowing. So why would you do that? And I think one of the more dangerous, the dangerous types to have in your function is the person who wants to build everything from scratch. It's an absolute luxury, I think in most commercial organizations to sit down and try and do that. And in like every profession, I think we in data have commoditized ourselves out of some jobs. You know, I think data science has been largely commoditized out because we built automated data science everywhere. Not for every solution, but for I would bet 80 20, you can just press a button on the tool and get a good as answer as you could if you set a team going on it. So I would say in most situations, build where there's a competitive advantage or uniqueness to your product or your industry or your solution that can't be got off the shelf and everything else buy and hook up and let somebody else take the pain and maintenance away from you. Great points. Thank you very much guys. So conscious time wrapping up today's session. I like first of all to thank you very much for your time and for this great panel. We made it very clear that a robust data culture is crucial for any organization looking to innovate and overall stay competitive. And my key takeaways from today's webinar include the importance of understanding your company's people. We talked about people throughout the full webinar and their individual challenges. The power of working together to ensure a strong data culture and the need for the integration of tools to be a force and unite employees and help them to try not to hinder behind. So again, thank you everyone also our audience to join us today. We hope we really hope you found this discussion as interesting as we did. And if you don't already make sure that you follow these guys on LinkedIn for more great content on this topic. And if you're interested in finding more out about I genius and what we do getting content with me on the member of our team and we will be happy to have a chat. So thank you very much and have a great rest of the day.