Andy Davis’ Post

Transcript

Why is insurance a great place to work specifically for Datsun? I think as a seasoned data professional, we spent 30 of my last 35 years in insurance. You know, clearly I had an easy paper round. It's not, I think as darling, as legacy as some people might think. I think that yes, it is a safe place to go in a disaster. I went there back, I went back to insurance during the middle of COVID. But actually there's loads and loads like startup scale ups doing really cool things in the insurance. Space with data in particular, there's also in any assurance, whether it's sort of like personal lines, commercial lines, all sorts of different insurance areas, there is loads of data. So you go to you go and work in insurance space, they will have loads of their own data and they have loads of external data that you can bring in, which is often also very exciting. Plus they as an industry, they have the money to invest in tech, in data and in in people. So I think it's a great place for data talent to go. And in line without really, I suppose from your perspective, where do you see, you know, some of the biggest opportunities for more effective data utilization within, within insurance? I think I probably spent and you know my time, you know, supporting lots of areas in insurance businesses with data, in particular marketing. And you always think, well, surely marketing is exhausted. I think marketing is there's so many new channels. You know, if you've said to me five years ago, you'd be measuring the effectiveness of Spotify, YouTube. Tick tock, you know, in an insurance that I'd have probably laughed at you and said, no, we won't be doing that sort of thing. And so I think there's there's always new opportunities within like existing like channels. The big one obviously is pricing. I think people, customers are wanting a sort of more exacting fairer open pricing methodology. But actually with the advent of so much data, it's quite hard to do that because you can be using lots and lots of bits of data to understand someone's risk. So I think there's lots in pricing. But the whole thing to me is going to get fueled by AI and without sort of, you know, sort of making a big thing of AI is powered by data and people's imagination. With those two things together, you can do some great things, but you can also do some really bad things if you have really poor data and maybe some poor people operating with the data that say, I think it matters opportunities, particularly in pricing. But I think I'm going to say AI and data and insurance companies is is really a big thing. Wondering if you can share a pivotal moment in your career journey that you feel it's hard to significant impact on on where you are now and, and the work that you're doing today. Yeah, I it's funny. I was that an event not that long ago and people were introducing themselves and it was with, it was with a group of year 8 pupils at school. So, you know, they, they were just, you know, interested or they were, they weren't interested. They were being told about data careers and people were introducing like where they came from, what their background was listening to everybody and like. You know, very few people set out age 13 or 14 and went, you know what, I'm going to work in data and most people fell into it and I'd say, well, did I fall into it? Actually, I a little bit, but probably stemmed from. I started a career in actuarial, which for those who don't know is all about the mathematics behind insurance and pensions investments. Is a little bit dull if I'm honest, but actually, you know, do meet some quite interesting people in the actual world. And I spent three years at university trying to qualify and then another 5-6 years after university doing exams. And I think the pivotal moment was when I won study afternoon. I found myself asleep on my pensions textbook because it was that uninteresting. And by then I think also I'd sort of like forged a bit of a career in using data analytics and building databases and data warehouses and supporting the other actors in particular with data because they needed someone who sat between the pure actuaries. You just said it's like this and the technology teams in those days who didn't understand what like this meant and no one right to write things down. So I think for me the thing was when I saw like pivoted, I suppose, and away from going down the career of being an actuary to actually. A date professional and I've never looked back you know, that's that's really was my moment it was strange because I gave up on something that actually age 1314 I come across this thing called an actuary and thought yeah, that's for me the highest paid professional. I think it was there at the time in the when you're at school outside of being a lawyer, usually lots of maths so that was my thing and so yeah, to actually in a way to give up on, you know my dream, I suppose but I found you know, I suppose a liking for data and I thought was good at it so. Yeah, that was my political moment, I suppose. Trust and definitely must be unique within the within the insurance world. That story on the machine. And do you know what? I've come across some other people, there are some other people who have done podcasts and said, I started life as an actuary and I was like, wow, really? It was like, there's more than one of us. How you approach challenges and setbacks in your professional life, you know, And I'm wondering if you do have a sort of go to your strategy for overcoming them. Yeah, I suppose. As a as a highly analytical person, as I get told by lots of psychologists, I think a lot, I analyse a lot, I plan a lot, I'm very logical, probably struggle with emotion and therefore I overcome it by you trying to use logic to make decisions. And I heard this stat this morning that someone said 85% of decisions are made emotionally and not rationally, which obviously is someone who's very logical when uses data. That's like a terrible thing to hear. But yeah, I suppose you know, lots of data people tend to be. Quite introverted tend to come from very technical backgrounds. And so one of the things about introverts is we tend to draw energy from within ourselves. So we're terrible at drawing it from others, getting others to help us. And that does mean we get to the end of the day in quite often we're exhausted because we other people draw on us and we draw upon ourselves to to, to get through a day and make decisions, I suppose. And I think the one thing I, yeah. So I think I do all those things to help me make decisions. You know, it's all about sort of thinking and planning. You know, literally coming up with options like should we do this, should we do that? What's the pros and cons? You know, I buried analytical approach to and a very unemotional decision making process. I suppose the one thing I think I look back and wished that I'd had as a data person was maybe having like a mentor to help me with my sort of career. I sort of look back and think I had some really good boxes early on and then pretty much from a certain point onwards almost had no one that was helping me move and why I sort of. Data analytics career, yeah. They just wanted me to manage the team, manage stuff, get stuff done. And whereas now I spend a lot of time mentoring or heads of data and spending time helping kids maybe enter data as a career at university or even at sort of high school age. So, yeah. So that's sort of, yeah, I wish I had that, which would help me probably to make decisions in my career perhaps better than maybe some of the decisions I have to make on a day-to-day basis in terms of proposing. You know this, that the other, etcetera.