Today's guest is the founder of Leaders of Analytics, podcast host and coauthor of Demystifying AI for the Enterprise. He holds a master's in international finance and accounts and has led analytics and data science teams across many sectors, including energy, banking and legal. He's also made the Cranium intelligence global Top 100 list for innovators in data and analytics and even served for five years on the Danish army. Welcome to the show, Jonas. And I have to say that is quite an impressive and diverse background you have there. Well, thank you for having me on the show, Janice. It's absolutely my pleasure to be here today. Now, Jonas, I feel like my intro can't really do you justice. Do you want to start by sharing your a bit more about your career background for our listeners and how you've gotten into your current role? Yeah, sure. So I'll probably skip the Army bit because that is almost another world to analytics. But I ended up in analytics in a roundabout way and it started about 17 years ago when that whole skill set and industry was very nascent and it actually happened by me just sending off an unsolicited application to a bank. And they all of a sudden had me in for a job interview. And before I knew it I was an analyst in a bank. And from there I learned how to code SQL and SAS, which were the big languages back then. And from there I've, I fell in love with the superpower that we have at our disposal in all the tasks that we housed in these organizations. So I had a background in finance and accounting. So I was numerate and literate in data and what to do with it. But what I saw here where we were using millions of roles of data really excited me to do more in that sort of arm of my skill set and as I moved to Australia, that was the thing that got me into the job market here. And then I climbed the corporate ladder, so to speak, and loved every bit of it. So I started in various analytics roles in finance, first in Australia, then I moved to energy. where I did proper analytics modeling, It's called data mining back in the day and I also did pricing and commercial management, and then I moved into banking again where I ended up as the head of analytics for We Bank in Australia. And then I had a stint in consulting after that as a consulting director in the analytics space. And then I was until recently the head of data science at Maurice Blackburn Lawyers. And at the moment I'm working full time on my own venture, which is called Leaders of Analytics, which is a brand that sort of encompasses a podcast, a newsletter and some self-paced digital courses, but also a training offerings for corporates to really hone the skills that a lot of data professionals lack, which is communication influencing and being able to tell a story with the data that they produce and actually get an outcome out of the wonderful products that they produce. So this is something that I've seen come up a lot in the last five years as the number one challenge for analytics teams to break through that they lack the ability tocome in and communicate with the business and drive outcomes. So I see this opportunity to help with that and to help train people to do that well so that we can all succeed as a community with data and analytics in the future. That's fantastic. I was actually at a conference last week and one of the keynote speakers talked about data storytelling and really using stories to help people connect with things beyond facts and figures. So it is a hot topic at the moment is really how to get those emotional connections to the data and make it meaningful for people. Now, Jonas you've had experience across so many different sectors and industries. Are there any really interesting business use cases that you've seen over that time for machine learning oranalytics that you're able to share with us? There are many,and I might sort of split that question into two is that there are things that I have done in every industry vertical that I've worked in, which is customer retention, customer acquisition, marketing, amplification, financial modeling, and also things like customer profitability, customer lifetime value. Sothose sorts of elements I think is the bread and butter of any data science or analytics team that that's sort of what gets you a seat at the table when we are talking about driving profitable outcomes for an organization, which is actually why we're there. But if I have to mention some more interesting perhaps projects that I've worked on, I can mention that, for example, during COVID 19, the pandemic we had one of the big challenges for insurance companies that I worked on for a while was understanding what would happen to insurance claims down the track now that they were almost no cars on the road. And how would that play out once cars came back and perhaps said that the traffic patterns had been altered because people were working more from home, having different driving patterns, etc.. And so one of the projects that I led there was actually modeling at postcode level the impact ontraffic in terms of volume, but also that the kinds of traffic and where it went at, what times of the day, which is once you aggregate that up, is quite a predictor of accidents and not just the volume of accidents, but also the kinds of accidents and therefore the kinds of severities of injuries that people sustain, which down the track some months or years later end up being insurance payouts. So pasting that whole thing together was quite an interesting project with lots of moving parts and complexity in it. Another interesting project that I recently did in Legal Services was using NLP modeling to pick out particular legal cases and from case notes and file notes that had been collected from conversations between the client and an a legal representative, and to actually classify these legal cases into a different kind of bucket, set the new different treatment. So again, the more advanced kind of stuff where you're using some modern techniques to do that kind of work, That's fascinating. I love hearing those and those real life business use cases and there's always things that you just you don't even consider, you know, data beingused for. The other question I wanted to ask you, I think it's really the hot topic oneveryone's lips right now. Large language models, generative AI. There's such a trend. You know, at the moment we're saying there's such an interest in the shift towards this. Is this something you think companies are seriously going to invest in? Andwhere do you see this going for organizations? So it's first of all, very exciting what's happening. But we are very early in the development of this. I think in ten years time it'll be very mainstream in corporates and you are expected to be able to use these tools in your day to day as a white collar worker. But it's still a very developing technology. It doesn't have the nice skin on it yet. It's not necessarily user friendly or as user friendly as it will be, but it will definitely come. So I think everyone who works in white collar jobs need to be familiar and comfortable with these sorts of tools, and they are super powerful. Personally, I use them every day more than once and it saves me lots of time. It saves me lots of mental capacity, which is equally important and it is only the start. So when we're five, ten years down the track, you will see these sorts of tools being deployed much more specifically to more particular use cases and therefore they'll be much more accurate and you'll start seeing them. This is my forecast, of course, but this is nevertheless what I think you'll start seeing these tools really start to chew away at some of the more administrative tasks thatwhite collar workers typically have and augment or take away large parts of their jobs. So things like customer service and so on. You could imagine that being at the very basic level, a large part of that being completely handled by these sorts of technologies. And I think as you said, you know, it is so early in this journey. I think they are powerful tools. We, you know, we don't fully appreciate, I think, whatwe can do with them. Andso it's a really exciting time, I think, for us to be in this industry and see how things evolve. Now Jonsa I think we're probably out of time. So I really wanted to thank you for making the time to come in and have a chat with me today. It'salways a pleasure to talk to you about this space. And for our viewers, you know, I'm wanted to thank you for tuning in once again, and I hope to see you all on the next episode. Thank you all and thanks. Jonas Thanks for having me, Janice. Really appreciate it.