 Hello, and welcome to My Career and Data, a podcast where we discuss with industry leaders and experts how they have built their careers. I'm your host, Shannon Kemp, and today we're talking to Peter van Joursveld from Oliver. With a robust catalog of courses offered on demand and industry-leading live online sessions throughout the year, the Dataversity Training Center is your launchpad for career success. Browse the complete catalog at training.dataversity.net and use code DVTOX for 20% off your purchase. Hello and welcome. My name is Shannon Kemp, and I'm the Chief Digital Officer at Dataversity, and this is My Career in Data, a Dataversity Talks podcast dedicated to learning from those who have careers in data management to understand how they got there and to be talking with people who help make those careers a little bit easier. To keep up to date in the latest in data management education, go to dataversity.net forward slash subscribe. Today we are joined by Peter van Joursveld, the Global Head of Production at Oliver, and normally this is where a podcast host would read a short bio of the guest, but in this podcast your bio is what we're here to talk about. Peter, hello and welcome. Thank you so much. Very nice to be here. I was so glad you could join us. So tell me, you're the Global Head of Production at Oliver. Let's start with Oliver. What kind of business is Oliver? So we're an advertising agency, but with a slight twist in that we focus on in-housing. So we build agencies inside our clients' businesses all over the world, and that's been our business model for the better part of the last 14 years. So add agencies as you know them, but from the inside. Oh, very nice. So as the Global Head of Production, what is it you do for Oliver? What's your typical work week look like? So it's I suppose no two weeks are ever alike, I suppose like anybody in this business. But my focus is very much on the content we create, both the quality of it as well as the effectiveness and the focus in recent years are shifted very much from efficiency to effectiveness. So creating content that performs in a marketing context. So my average week, a lot of my time is spent with clients on solutioning, on solution design. A GNAI and AI over the last couple of months has really changed the nature of how we work. And there's a lot of time spent with our clients on understanding how that impacts them, their work and where this will take them. So that's a big focus at the moment. And then the other side of that is working with the rest of the organization to make sure as a business, we're ready for what really is a transformative change in how we do the work that we do. Oh, that's very cool. So how is generative AI changing things for you? Well, it's a very interesting topic in that, you know, we've been working with generative AI as far back as 2015, 2016, you know, we started our first kind of client pilots back in 2018. But obviously in the last year or so since ChatGPT, you know, really kind of captured the public's attention, you know, that's just completely supercharged the focus on that. And, you know, we generally when we start working with clients to understand AI and GNAI, the focus immediately shifts towards efficiency. You know, the assumption not wrongly is that AI will make us more efficient than it will absolutely and it is making us more efficient. But, you know, a lot of the work we do is focused on what can we do? What do we do with that efficiency? Because if you think of it, you know, through a marketing lens, you know, marketing is all about differentiation. It's all about helping our customers stand out, you know, and achieve their goals. So if the efficiency benefits of AI kind of represents table stakes to some degree, everybody becomes more efficient. Then the question is, what do we do with that headroom? So a lot of our work is focused on how do we unlock, you know, additional value? How do we find new things to do with that with that headroom? And that could be personalization. It could be more volume. It could be new markets. So, you know, for us, it's very much about understanding how can we, I suppose, work on both sides of that coin. And, you know, the other side of that, I suppose, is how do we do it safely and ethically, you know, slowly starting to see the green shoots of governance, you know, come through in the EU and in the US and other places. But there's no, you know, consistent global governance framework yet. So a lot of our work is also focused on understanding how can we best advise our clients and, you know, set up our business to use Gen AI safely, ethically, and, you know, in a way that will be sustainable as, you know, governance does increase. I'm so glad you mix in ethics with that in that mix. It's such become such a hot topic along with Gen AI, right? Like you say, the security and the ethics of what you should and shouldn't put in and how to use it. Yeah. It's, you know, it's so interesting. We've had the most interesting sort of case studies where just because you can do something with AI, should you do it, you know, things like when you start generating virtual humans, you know, should we be generating the virtual humans, you know, from a, from a visual perspective, when we, you know, create chat bots that are now becoming almost indistinguishable from, you know, at least a, you know, chat conversation, do we need to disclose, should we disclose that, you know, the consumer is interacting with, with, with, with AI. Now the EU governance, you know, guidelines, disclosure is a big part of that. So, you know, that will, I think, become probably legal requirements in most territories, but, you know, at the moment, it's about advising our clients and working with them to say, as a brand, whatever you do, you want to make sure that you don't, you know, regret later on, you know, it's, yeah, so it's a very interesting kind of space from that perspective right now. And I suppose we don't talk about it that much. We focus a lot on the technology, but not as much on the ethics and the, the change management. Very nice. So let's back it up then a little bit here before we get too much more into your, into what you're currently doing. So tell me, Oliver, you know, when you were, say, like six years old, was this the dream? Did you say I'm going to be the global head of production at, at, at Oliver when I grew up? What was the dream? No, it's sort of the coolest thing because I suppose a lot of the jobs we do today didn't exist when we were six years old, you know, which is, which is, which is quite fun. I suppose that's the benefit of living in the future. You know, when, you know, when, when I was six years old, I remember, you know, seeing in a book that one day people would have, you know, watches and phones on their arms, you know, and here we are with, you know, Apple watches completely, you know, normally, so I suppose we are living in the future. But no, I wouldn't say it's necessarily, you know, what I'd set out to do. But at the same time, I wasn't one of those kids that, you know, had a, a vision for exactly what I wanted to do. I was as most fortunate in that I was very kind of generally capable and interested in lots of things, very curious, very kind of agnostic. And we did, you know, all sorts of kind of career guidance and counseling sessions. And when we did those, the advice I always got as a teenager, you know, it sort of ranged from everything from, you know, going to finance to going into horticulture, you know, you sort of all this, you know, some, some fair extremes, where do you end up? So I suppose I'm kind of lucky in that I've ended up in a space where I can be both creative and analytical. And my career has been sort of a pendulum that swings into very creative for a couple of years, and then more into analytical. And I'm sort of in a place now where I'm fairly happily kind of in the middle of that. So you're definitely, I wouldn't say it was where I'd set out to get to, but I think it's probably more interesting than what I would have said when I was six years old. I definitely understand. And I can relate to that. I love having both the creative and the analytical. I need both in my job. Absolutely. Yeah. So then as you were going then through school and such, and as you started developing some passions, you know, where did you start heading? I hit it very much, I suppose, creative first. And I suppose, you know, as a as a teenager, you sort of drawn to, you know, I was drawn to wanting to, to make things and create things. And, you know, the way I sort of started was a mix of I was always kind of quite capable technically, you know, and this wasn't sort of early days of I suppose, personal computers, you know, lots of, you know, there wasn't a personal computer in everybody's homes, you know, we were fortunate we had, you know, computers from when I was fairly young. And you know, so I sort of tinker and, you know, experiment. And I remember the first, the first program I wrote in basic, and I must have been, I don't know, must have been about 12, 13, 14, I try to write a chatbot in basic, which in hindsight is obviously pretty funny, considering where we are today. But also a little bit embarrassing to think that, you know, 12 year old back then was going to write a viable chatbot in basic and the conversation flow was obviously as linear as you would expected it to be. But, you know, I suppose that kind of tinkering, you know, didn't really lead me into a career in computer science, it sort of just gave me the broad capability to latch onto the sort of desktop video revolution as that kind of really picked up in the late 90s. And so for the first sort of five, 10 years of my career, I very much worked in post production in visual effects in desktop video production, and that then expanded into film production, then into advertising and sort of slowly or organically got here. But, yeah, I think when I was younger, the creative draw was definitely, I suppose the more interesting side of things, but it was enabled by the technical capability. I think that's maybe an important thing that when I work with with with people today is, you know, having that technical ability and grounding has been super useful to allow me to do creative things. It sort of has been an enabler, if that makes sense. Ah, yeah, absolutely. What was a what was the first job? Um, I actually couldn't quite get a job. So I managed to basically blag my way into a video production company as a runner and a general, you know, IT person and computer fixer and, you know, phone answer and kind of either pity or guilt that they started paying me after a couple of months. And and that then that that's that's where I then I became eventually became an editor there. And I was there for a couple of years and sort of I suppose got the got my firm grounding there. And this is, like I said, in the late 90s. So everything you learned back then you learned either through trial and error or by reading manuals. Actually, it was more trial and error than reading manuals. But, you know, it was just what's the word? It was just sort of kind of sheer hard work and just experimentation and going, well, let's take the next path and see if that goes anywhere. Okay, well, that's a dead end. Let's try this. Let's try, you know, and get somewhere I suppose maybe growing up in the early days of of of gaming and computer games when it was, you know, King's Quest and Space Quest and those sort of giving me away my age here. But you know, where you basically have to just bash your head against the wall until the wall gives away was a very good grounding to kind of get that early, you know, kind of career going. I love that a lot. So I love that you just picked a company deciding you're going to go after it and just did whatever it took to get there. Yeah, in hindsight, maybe not the most elegant approach, but but it worked. It worked. Yeah. So so then so then so after so then you moved. So where did you go from there? I suppose from there, you know, I got kind of caught into into the romance of high and visual effects and really, so I grew up in South Africa and we had a very good film industry in South Africa, but also a very small one. And again, it's sort of a story of how my technical ability, I suppose, helped open doors. Then it still exists. Actually, there was a compositing system called Flame, which, you know, back then, there were about two or three of those in South Africa, they were they were hugely expensive. A day of working within the system, you know, we would essentially rent it out by the day to clients. And the equivalent back then was, I think, in a week's worth of work in the system, you could buy an average family home. That was a sort of, you know, the day rate that we would get kind of charged out at. But it was highly, highly technical. So again, my technical ability kind of allowed me to get in there at a very at quite a young age. And that allowed me to then build up the contacts in the creative industries to then start expanding into, into film production. So I went from there. So individual effects, I directed commercials for a while. And then actually, no, I'm skipping, I'm skipping over a step. Before I directed commercials. One of the big changes that we've seen was as I was working on the system called Flame, hugely expensive, very, very proprietary. And most projects didn't have the budgets to be able to spend much time in there. And Adobe had just released After Effects. I think it was After Effects version two or After Effects version three. And it was the first version of After Effects that had become viable as a alternative to this very, very high end system. And you could spend a week in After Effects for the same price as a day in Flame. And, you know, it wasn't a one to one relationship in terms of what you could achieve. But the math made sense. I started a small company that essentially took advantage of us. So did that as a, did that for a couple of years, eventually, exit to that, and then went into directing commercials, which I thought was, you know, that was the dream. I wanted to be a commercials director. That was an interesting time. It was a very creative time, but not a great way to pay the bills. You know, so, because in your, this is my late 20s, you know, so I've always been a big believer that, especially in your 20s, you should try lots of things. And if you're going to make, you know, mistakes, you can take wrong turns. Your 20s is when you want to do that, because you've got the most room to still course correct as you go. So I suppose, you know, my 20s was a lot of that, you know, trying things that, you know, I thought, you know, that was the dream, you know, photography and being a commercials director. And I had huge amounts of fun doing it. But it just wasn't a, I didn't have the, it just didn't work. I mean, I quite frankly wasn't good enough from a creative perspective, you know, to really make that a career, I don't think. And, you know, you learn some hard lessons as you do that. And kind of out of that, I then was late 2007 and through a contact I've worked with on all for a couple of years, he had this idea to start a post production company inside an ad agency. And it's like, well, that sounds like an interesting idea. Let, well, we'll try that out. And basically, that we started back in 2007. And that eventually became a business that we sold to Oliver and now Oliver. And that idea of starting, you know, doing again, taking advantage of technology, putting that technology into our client's world, again, open doors. And I suppose, you know, I've never kind of told the story in this way, but you sort of look at that story, it's interesting how the technical ability has unlocked all these sort of doors to do what I really want to do, which is, you know, quite handy. Yeah. Yeah. I'd say so. Indeed. I love the persistence too. And I love the trial and error. And, and you're right, you know, I mean, 20s is a great time to try things and fail and learn and grow. And yeah. So I love that you weren't afraid to do that. Yeah. And I think I mean, we don't talk, you know, there's lots of, you know, you read often, you know, about how we should embrace failure more. And, you know, I really hope that we do learn that lesson. I mean, it's a very hard lesson when you're in your 20s, because, you know, you may be done with the perspective yet every mistake you make in your 20s feels cataclysmic. You know, how will you ever come back from this? You know, that's about this, you know, sitting here getting to, you know, the wrong side of 40, you know, you kind of get to look back and get actually, you know, it's not that cataclysmic. It probably, you know, it probably will work out. And but I think, you know, that that that persistence you mentioned, I think is hugely important to try new things is really, really important. And just, yeah, just dusting yourself off and going again. And you know, that's even if you don't necessarily know, like I said, at the beginning, I never really had a career path in mind. But I had, you know, I have things that that that that give me energy that feed me that I find interesting that I want to explore. And, you know, letting that sort of be the guiding light actually in the long run has worked out really, really well. And it's allowed me to do really interesting things. And in the end, you know, this company that we started in 2007, once that bolted momentum and starts getting a bit of scale to it, I actually spent a lot of years being incredibly creative. It allowed me to actually be to really, really creative work, really good work, award winning work. And, you know, if I didn't have that, I suppose that platform, I probably wouldn't actually have achieved that. So yeah, that persistence is underrated. Ah, yes, indeed. And congrats on the awards. What awards did you win? It was interestingly things like craft awards for editing. We won some awards for some AVs we created. And we we sort of developed a reputation for doing just really compelling lots of B2B type work, creating very, very first innovative. A lot of it was and just compelling work in that space. But, you know, we used to joke that, you know, we don't need awards. We've got clients, but, you know, it's always nice to kind of look back and, you know, have that to have that validation is is is always useful. Yeah, absolutely. Well, congrats on that. And so tell me then, you know, being in it, how do you currently in your throughout and throughout your career? I mean, you talked about generative AI and stuff. How do you use data to make it through your work and with your customers? Yeah, it's really, it's really interesting, you know, in the early part of my career, you know, data really was content, you know, it was the early days of the of the desktop kind of revolution in DTP and then in video and then other things, you know, so data for us back then was all about how to maximize storage space action was the biggest issue we had back there, you know, without how could we, you know, get, you know, maintain the quality levels that we wanted, you know, while, you know, so data was very functional back then to us. It was very much a tool that just allowed us to create the work that we do. And over time, it's sort of gone from content to data driving insights and now data drives performance for us in both performance in the effectiveness of the work that we do, but also performance in our organization. So I think I suppose that's sort of the really great thing about data is that it just, it's just so many facets to it that, you know, that ability to understand data and work with it. I want to say almost at an abstract level is incredibly important and incredibly useful almost ahead of your ability to work at it at a more specific or concrete level as an analyst or as a developer or as a, you know, because what you learn over time, and I suppose having this background of both being creative and analytical is that there's always patterned to data and there's always principles to how data performs that actually applies no matter what you do, which I think has been really, really interesting. And, you know, so for us today, one of the challenges we've got is, you know, people used to joke that, you know, in marketing, the, you know, that 50% of your marketing budget works, you just don't know which 50%. Now, today, we're heading into into a world where we actually do understand which 50% is working. So we're getting into a world where we're really starting to optimize every piece of content that we do, every media, you know, dollar that we spend that's optimized to, you know, to really maximize the effectiveness of that spend. So we're getting a really good view on what kind of content forms well in the market, you know, and drives the key metrics. The flip side of that, and this is where AI is comes in, is that starts driving towards convergence because a lot of the measures, you know, are maybe not that sophisticated yet. So once something works, there's a tendency to simply iterate, you know, and maybe you may adjust and iterate, but you tend to iterate and then slowly you start converting. And the challenge for us now is to create work that we believe will perform, but not conform if that makes sense. And that's a really challenging kind of mindset for a lot of people to adopt is a world where even though the data, you know, says, I don't know, make it magenta per argument, say understanding actually the data is not saying make it magenta, there's a message underneath, you know, what currently is coming up as magenta and understanding how do we carry on differentiating with, you know, from that perspective. So I think data is a slightly twee to say, but it's kind of the fuel, right? It's kind of now really that the oxygen that drives everything that we do. And I think I don't know if the marketing services industry, you know, has been slow to, you know, kind of come around to that. But today, compared to even five years ago, the amount that we use data, obviously in the effectiveness of the work that we do, like I just said, but also in the transparency of how we do the work. So one of the things for us as an in-house agency, one of our core values is transparency. The benefit of being in our client's world is that we have a deep understanding and empathy for their world. But, you know, you earn that empathy and that trust through transparency, which means that, you know, we're very, very transparent about our talent utilisation, the effectiveness of the work that we do, et cetera. And, you know, that all comes down to tons of data points around how people are spending their time, you know, what are the tools that we're using? Are those the best tools? Are our workflows optimised? And there's a lot of, you know, quantitative data that we now work with to also look at how can we optimise our operations to ultimately, you know, deliver better value. So data is literally two voices everywhere. Whereas, you know, 20 years ago it was very much focused on, it was, it was, it was the word slightly less romantic, it was really just about optimising space. More and more companies are considering investing in data literacy education, but still have questions about its value, purpose and how to get the ball rolling. Introducing the newest monthly webinar series from Dataversity, Elevating Enterprise Data Literacy, where we discuss the landscape of data literacy and answer your burning questions. Learn more about this new series and register for free at dataversity.net. Indeed, yeah. And so, how would that, so, how then would you define data? I mean, aside, you mentioned that, you know, it's the oxygen, you know, and it is, you know, but how would you define data as itself? It's a really interesting question. So one of the big things we've been working on over the last sort of year or two is connecting, I suppose, different bits of data into more meaningful, one of the iterations would be dashboard for us and for our clients to understand how the, to help see, as opposed to the underlying relationships between, you know, between things. So I think, you know, how would I define data? It really, it depends so much on through which lens that we're looking at it. If we're looking from our customer's lens, you know, that focuses massively on the effectiveness of the work. So, you know, did we move, you know, the, the, you know, cost per acquisition? Did we move the click-throughs? Did we move, you know, the key marketing metrics and then to support that, what would it cost to move those metrics? You know, so if we moved something by 20%, what was the investment to create that move by 20% because of the, you know, at some point, there's a crossover point where actually investment simply doesn't make sense for the, for the, for the way that we've moved the KPI. So, you know, in our client's world, it's very much focused on that trade-off between cost and, and performance. For us internally, it's much more about optimizing the, the engine that runs it. And that's about maximizing our resources, be that our people, our infrastructure, you know, or, or our technology. But the way all of this really kind of comes together is, you know, through much more, you know, getting data into people's hands and helping them, helping equip them to interpret that data because that's the other side of this. The data is one thing, but ultimately, you know, as we sit here today, we need people to be able to interpret that data and make it actionable because data, you know, without making it actionable, without having a strategic, you know, imperative that it can help us deliver, the data is essentially worthless. I suppose that's maybe, you know, we're talking about what it means for us is really about figuring out how do we make data actionable. And how, you know, how do we, that's really, I think the, the heart of it today. Love hearing how much, how much data plays an important role in that creative environment. Right? Yeah. Look, I mean, I wouldn't say it's always easy to, you know, there's a, you know, speaking to you, it's slightly easier to maybe position it as such. You know, I think there's always, there's always a tension. That's a healthy tension between, you know, between creative and the data, you know, and unique that I think something that I suppose, you know, the one thing with data, like I said, being able to interpret data as incredibly important, but, you know, I think for, for this audience, I think we also all know that data can be, can be, can be manipulated, or at least the positioning of, you know, because it lies, then lies in statistics, you know, you know, so being very aware that you can't always interpret data at face value. You do need to kind of look, you know, at the underlying metrics. I had a CEO years ago who, when we were going through management reports, would always, he had this complete and, and allergic reaction to percentages. And he always used to say, if you want to lie, use percentages, show me the quantum, show me the, show me the actual numbers. And I can understand the relationship of, you know, of the actual, of the data, not of, of, because the percentage is the narrative we're trying to tell you, you know, and which I thought, you know, is always stuck with me. And I think, as, you know, we talk about that tension between creative and data, you know, sometimes a creative's hunch is more powerful than the data would, would imply, you know, because it can see things or it can feel things that maybe the data doesn't support today. You know, but in hindsight, will support. And I think, you know, my point about convergence earlier, one of the challenges we're starting to see is, you know, we're using analytics tools to help predict the performance of the work that we do. But what's really, really important is to be aware of the fact that it's using past performance to predict likely future performance. And that sort of feeds the convergence to do. And at some point, we have to diverge. Otherwise, you know, we will just sort of sink into the sea of sameness. But to really come back to my point about being able to interpret and understand data is really, really important because as much as we want to give people actionable data, you also have to equip them with the ability to question the data and go, actually, hang on, should I skin this differently? We, you know, what is the message in there? Yeah. Oh, I love that. And, you know, I think that's common across any industry. Right. I agree. Yeah. Yeah. Absolutely. You know, sort of saying earlier about kind of patterns in data, and, you know, I think one of the beautiful things about it is that when we start looking at data and we compare it across different segments of the business, it tend, they tend to perform in similar ways, which is really, really interesting, you know, and, you know, outliers are outliers and, you know, anomalies, you can, you can normally spot them fairly easily, even when you, you know, when you look at data from finance or you look at data from marketing performance, there's, like I said, a relationship that, that you can kind of learn to, to see which, I mean, like I said, maybe slightly abstract and, and, you know, what's the word, and ethereal for, for this audience, because, you know, we do like data, but I also very firmly believe that when you analyze it, you can sometimes just, you can sort of see those patterns that, you know, exist there. So, yeah, I think it just, it is about trying to, just connect those dots. Maybe that's the, kind of just a bit, yeah. Yeah, yeah, indeed. So, tell me, do you see the importance of data? And I could think I can formulate already, be so a lot of your answers already, but, but do you see the importance of data management and the number of jobs working with data, increasing and decreasing over the next 10 years, and why? So interesting. And I mean, it's obviously a two-part question, and the first part is it has to increase, right? It has to, the importance of data, I don't see decreasing. The only way in which I see a decreasing is we're relying more on the systems to tell us how to interpret the data, and that's fine, but, you know, when you're looking for opportunity and you're looking for the, for those points of divergence that can help you differentiate, the systems may not always be the best place. You know, when I say the systems, you know, looking at things through a very generic lens. So, you know, from my perspective, I would like to think that the number of jobs will increase because data is going to become really, really important. And I think from up in our industry, you know, one of the big things we're expecting GNI to do is create this flood of content, because, you know, content creation is becoming very, very accessible. And that means that in turn, differentiation is going to become harder and harder. So finding ways to sort of cut through that kind of noise layer is going to become really important. So within our industry, you know, I would like to believe that, you know, the need for data jobs will increase. You know, and I think it's an interesting thing. I used to say that, you know, I wanted all of our people in our business when we were we were relatively small back then, and a couple of hundred people. I wanted all of them to go on basic analytics and Excel courses so they could at least get a grasp of data and how to interpret it. And this was, you know, creators and everybody else. And I think the reason for me for that, and I still feel that way today is that I feel like the ability to interpret and analyze data sits in this little niche over here. And that niche is generally put in a room over there. And it doesn't mix in our business with creative or with strategy or with and I just think it's such a miss. It's just such a big miss in terms of the real unlock and the real opportunity that the ability to analyze and understand data gives us. But to my earlier point that only works if we sort of fight against the potential. What's the word? You know, laziness or convenience that analytics tools give us because analytics tools are incredibly powerful and incredibly useful. You can query Excel in ways now that, you know, you just never could, you know, five and 10 years ago, we get to manually, you know, you know, you know, create your queries and your reports. And there's a benefit to that because it's meant that data is not accessible to everybody. But again, that becomes dangerous if people don't understand what that means, how to interpret it, how to use it. Because without that, you know, an anomaly can very quickly be, you know, want to say weaponized, weaponized your anomalies. There's something in there. Hopefully not weaponizing the anomalies. But so, I believe that we are going to see an increase in the need for data related or data knowledge in potentially more generic roles, maybe more so than pure data, you know, jobs increasing, if that makes sense. That makes a lot of sense. And I love that answer. And don't disagree with that with so many initiatives out there for, you know, what's been branded data literacy, right? There's been a lot of controversy over that brand, you know, because the negative of that, if you're not data literate, you're illiterate, which is not necessarily where companies want to go, but, you know, or, you know, be accused of, but building that awareness and data knowledge and skills into every job is, you're right, it's so important. And something that- But it's everything you want. Oh, sorry. You go ahead. Let's say, it's important in our jobs, but I think more and more it's also important outside of our jobs, you know, because what that gives you is the ability to question, you know, the information that you're given, you know, I don't know, pick a random statistic, you know, I don't know, 300,000 jobs lost in Q1. That's a terrible statistic, but within which context, you know, globally, you know, within, you know, the Pacific North, you know, like what- And just stopping and just going, hang on, you know, and as, like I said earlier, we're expecting AI and the ease with which content can be created to obviously drastically increase the amount of content created. It's also going to become an incredibly powerful tool from a, I mean, I don't want to use the word misinformation or propaganda, but the problem is, you know, bad actors will act badly, you know, that's a reality. So the ability to understand the information that you presented with interpreted, question it and look under it is hugely important professionally, but I believe it's going to be even more important for us personally to actually go, hang on, you know, this doesn't actually, this doesn't make sense, you know, and that's how that's about, ultimately, you know, data literacy, like I said, maybe a slightly inflammatory term, but the ability to understand and interpret data is something, quite frankly, we should be teaching kids at school, you know, because it's just, it's going to be so important, it is so important. The ability to look through and question and analyze and rationalize what you're being presented with is either real or not real. It's not a data-related example, maybe it is a data-related example, but one of the, kind of a few months ago in the, you know, some generative AI examples of the Pope in a puffer jacket, I'm sure you would have seen those, you know, kind of make their way in a social media. And the most interesting conversation where, you know, lots of people, you know, thought what the Pope in a puffer jacket, that's ridiculous. And I had a client who actually said to me, the thing that caught her eye, and she knows nothing about AI, nothing about technology, the thing that caught her eye was, she knew the Pope was in hospital a week ago. Why was he on a beach in a puffer jacket? And that ability, because there's not really a data anecdote, that ability to connect the dots and see the relationship between those and then go, hang on, this doesn't add up. I think it's hugely important. And I think data is one of those tools that allows us to do that. If that, if that, yeah. Wow. Yeah, indeed. And gosh, you're so right. I mean, so much misinformation out there now, right? And with the use of, with the use of data, I had a friend to test out, chat GPT for to, you know, produce a data science paper. Yeah. And, and well, wow, these are some really great points that they brought up. Great, what, and as chapter GPT, what's your sources? And chat GPT, listed a bunch of sources. Well, it turns out, when she looked them up, all of the sources were fake. Yeah. And, but it does it so convincingly. Right. You know, that's, yeah, it's so confident. And it's so, yeah. And, you know, it's that, you know, that that's sort of the point for me is, I suppose maybe the point of this conversation is that, you know, what we consider data and analytics as an industry and a vocation needs to move. It needs to diffuse into our organizations and into our society. You know, yes, we still need data science. We still need deep specialisms. But at the same time, that general level of understanding has to start diffusing because. That's, yeah, I mean, like you said, you know, chat GPT does hallucinate and it hallucinates incredibly confidently. You know, and if you don't check your sources or if you don't question, you will just, you know, go along with it. Yeah. Oh, yeah. It's a really cool tool, really less fun to use, but check your sources. Exactly. Yeah. So what advice then would you give to people looking to get into career in data? So it's interesting. I suppose maybe it's because of, because my background has been so organic and, and a little bit, you know, sort of loose is probably the word I would use and, you know, I always say to my staff, you know, very rarely when we're faced with an issue is the issue in front of us, the issue I always say it's the stuff around the stuff, you know, and I suppose my, my advice to anybody wanting to get into a career in data is probably to try as much as possible and feed both the left and the right, you know, of the brain and, you know, get that good technical grounding and understanding how to analyze and work with data and get comfortable with, you know, how, you know, the tools that allow you to pass and analyze and experiment and mess with data almost kind of generically, you know, like learning Python, it's like a really good example of just being able to just manipulate things really quickly and just, you know, kind of fiddle around and try things in experiment. But I would, my advice would be to pair that with, you know, something that is probably slightly more on the humanity side and slightly more on the creative side or something slightly more on the, the applications or practical side so that whatever you do, you're able to take that, you know, and bring it, I suppose more, you know, into, into the light and, you know, you look at some of the examples we're seeing with AI in, in the healthcare industries at the moment, there's a study recently, a 10 second clip having between 86 and 89% accuracy on whether or not, of your voice, sorry, a 10 second clip of your voice having a between 86 and 89% level accuracy on whether or not you have type 2 diabetes. That's amazing. It is. A 10th recording of your voice can with 80 plus percent accuracy tell you if you have type 2 diabetes and it does that in seconds and for a cost in the sense. The nearest level of accuracy is in the dollars as an alternative to that and that's sort of my point about having the analytics and the data side, you know, understood and sorted, but figuring out how do you want to try and apply that to the world? And we're seeing these amazing things like I said happening in healthcare, I saw another one today actually about a tool that, you know, now uses your smartphone camera to rate the likelihood of skin lesions being cancerous, you know, and doing that with incredible levels of accuracy. And that only happens when you're able to apply data within a healthcare context or apply data within, you know, another context. Because then that's maybe my advice would be to figure out, you know, what you're passionate about and what you're interested in. And then I suppose much like I did, use data and use, you know, your technical ability to then help you kind of forge a path through that because that's A, I think where you get to do really interesting things and B, hopefully where you could do really meaningful things. Oh, that's such great advice. And that is the fun thing about data, right? It's across every industry. Yeah, it literally, it is everywhere in everything in some way. It's amazing. And yeah, drag it, let's get it into, let's get that benefit, you know, into the light. And you know, yeah. Yeah. Oh, Peter, this has been so much fun. So, but I would be remiss if I didn't ask, you know, if somebody wanted to find out more about Oliver and solicit your services, how would they do that? Easiest thing is probably to find us on LinkedIn or reach out to me and we'll get the right people in touch. Otherwise, look us up at oliver.agency. Nice. We'll get those links added to the podcast page as well. So everybody can find those easily. So Peter, thank you so much for taking the time to chat with us today. Amazing. I hope there wasn't too much for roller coaster. Yeah, that's great. Great conversation. Great information. I love it. It's very, you work in a very different industry than we've talked to so far. So it's really, really interesting how it's the same kind of, it's universal, well, data is universal. So it just goes to show. And I really appreciate that. And I love the aspect of blending that creative with the analytical. Thank you. Amazing. Love it. So nice to chat. And thank you very much. Thank you. And thank you to all of our listeners out there. If you'd like to keep up to date on the latest podcasts and in the latest in data management education, we go to dataversity.net forward slash subscribe. Until next time and stay curious of people. Thank you for listening to Dataversity Talks, a podcast brought to you by Dataversity. 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