How AI and Data Are Reshaping the Insurance Landscape with David Tuppen, Chief Data Officer at Enstar

Speaker A: 00:00:00

Foreign.

Speaker B: 00:00:12

Hello, and welcome to beyond the Desk, the podcast where I take a deep dive into the careers of some of the most influential and inspiring leaders in the technology transformation and operations space within global insurance and insurtech.

I'm your host, Mark Thomas, and every week I'll be sitting down with industry trailblazers who are driving innovation and modernization within the insurance sector.

We'll explore their personal journeys, from their early backgrounds and the pivotal moments that shape their careers to the challenges they've had to overcome, the lessons they've learned along the way, and of course, the big wins that have defined their professional journey so far. But it's not just about their successes.

It's about what you and I can take away from their experiences and the advice they have for anyone wanting to follow in similar footsteps.

Speaker C: 00:01:03

Whether you're just starting out or looking.

Speaker B: 00:01:05

To level up your career in the insurance or insuretech world, this podcast is packed with valuable insights and inspiration. So grab your headphones, get comfortable, and let's jump into beyond the Desk.

Speaker C: 00:01:21

David, welcome to the podcast. How you doing?

Speaker A: 00:01:23

All good.

Speaker C: 00:01:24

So if we kick off with just a quick intro about you, who you are, what you're doing at the moment.

Speaker A: 00:01:30

Quick intro. So, yeah, David's happened. So I'm. I'm a chief data officer within the insurance industry. Come from consulting.

I'm originally from South Africa, hence the, hence the accent. But yeah, I've been here 23 years now and moved around a fair bit around Europe and Bermuda and Switzerland and whatnot. But.

But yeah, that's me, all data. So I've actually started with data, ended with data. So that's my realm for the time.

Speaker C: 00:01:55

Being and see where it goes.

Speaker A: 00:01:56

Yeah, maybe. Yeah.

Speaker C: 00:01:58

So let's go right back to the start then. So obviously, data throughout all of your career. Yeah, how like what I always like to get into is kind of how it first came about.

How did you first find the kind of passion for technology? Was it, was it something from a very young age or did it kind of evolve?

Speaker A: 00:02:13

No, no, no.

Speaker C: 00:02:14

So I, when we're about to South Africa as well.

Speaker A: 00:02:17

Cape Town.

Speaker C: 00:02:18

Okay.

Speaker A: 00:02:18

So I went to school in Cape Town. I. I wasn't most studious of kids. I. I wasn't into tech really. No. After school I was going to study interior design.

Called around in Cape Town, a few studios and they told me there's no money in, in interior design. So I called a friend and said, what are you doing? And he said, I'm going into it. So I went, okay, fine. And I called up the college.

And I said, can I register? And I mean, I don't come from money.

So my father had to take out alone to buy my first PC, which I don't go back to the days where everyone talks about. I used to have a Commodore 64. I don't. I'm. My first PC was a P2 350, I remember. But yeah, that's, that's. That's how it started. I mean, I started.

I wasn't into tech. I started studying because my friends were studying it and then just, just kind of fell into it and I landed on data and I enjoyed data ever since.

Yeah, so.

Speaker C: 00:03:09

So do you. So you. So you actually found data quite early on in that when you. When you're at university is a kind of what you'd major in.

Speaker A: 00:03:15

Yeah, so. Well, so I studied, I studied. I t. I decided to do a. It was a big thing back in the day doing your Microsoft qualifications.

So I did MCSD after I did my, my diploma. My first job after South Africa. So my first job in South Africa was. Was actually doing like mobile development somewhat.

Back in the day it was WAP development.

Speaker D: 00:03:33

Yeah.

Speaker A: 00:03:34

And then moved to. I got a job in the UK doing bi. So I did a lot of business intelligence when.

So when reporting services and there was SQL 6.5 and then 7 back in the day. BI wasn't a thing really back then. So when it first came out, that's when I started using it and you know, it became really popular.

So that's where I found my niche was straight into sequel, then into bi, and then I kind of moved on from there.

Speaker D: 00:03:58

Yeah.

Speaker C: 00:03:58

Okay. So. So you came to the UK quite early on then?

Speaker A: 00:04:01

Yeah,: 2003

Speaker C: 00:04:03

So how old were you then? Give away your age.

Speaker A: 00:04:05

Sorry, I was 23.

Speaker C: 00:04:07

Wow. So what was the. What was the kind of catalyst behind coming to the uk?

Speaker A: 00:04:12

I mean, that's a good question. When I was. So I worked in South Africa for. What I was there for about.

I say I was there for a year and a half when I first had my first job and they sent me. They asked. But they sent me to the Philippines for about a year and a half, asked me if I wanted to go work there. Doing the same type of work.

Yeah, doing dev work in, in the Philippines. Said yes. And I got the bug to travel. My father's British, so I was essentially born with a passport. I had my British passport from, from birth.

And I thought, well, I'll just fly over and see if I. If I. Like there was no intention of Staying? Yeah, I just flew over and ended up being, you know, 20 odd years, 21 years.

Speaker C: 00:04:48

So your parents still in South Africa now?

Speaker A: 00:04:50

No, they've moved to the uk. They moved, they live down south in, in, in Kent.

Speaker C: 00:04:54

Oh wow. Amazing. So, so let's go through those kind of early years and so came in, started doing bi.

Was that in, was that in insurance then or like what, what did that look like?

Speaker A: 00:05:03

No, that was, that was, that was at Accenture, that was working at National Grid.

Speaker C: 00:05:06

Right.

Speaker A: 00:05:07

For the National Grid account and Yeah, I mean it was just, it was thrown straight into. They were looking for a SQL developer to support a solution and, and I came in as a junior dev.

Speaker D: 00:05:15

Yeah.

Speaker C: 00:05:16

So and then how, what did that evolution look like into kind of senior role?

Because that's to me is always really an interesting thing like that, that balance of moving from a kind of a techie as a hands on person to more leadership roles. Was that kind of an organic evolution for you or was it something you always wanted to do?

Speaker A: 00:05:32

Well, it is something I always wanted to do and I always had a plan for this.

Speaker D: 00:05:35

Yeah.

Speaker A: 00:05:35

So you know, I had a goal and to work to where I am and I've somewhat achieved what I wanted to get to. Yeah. And it was that, I mean it was a junior dev. I mean it's a, it's a straight cv.

Speaker D: 00:05:46

Right.

Speaker A: 00:05:46

I was, I was junior dev. I moved into senior developments, I became a data architect from data Architecture, I moved into consulting from consulting, I moved into sales.

So I knew the, you know, the business side of things from sales and then moved into, to industry. There's a few jumps in between when I was in industry but it was a progression from junior through to management through to where I am now.

Speaker D: 00:06:06

Yeah.

Speaker C: 00:06:07

So did you, the, the what, what did you, what was that plan right from the start? Was it always that you wanted to become a cdo? That was always the kind of ambition.

Speaker A: 00:06:16

Yeah.

Speaker C: 00:06:17

Or was it more the management piece? Like, I mean what, what, what, what bit did that? Was it that kind of.

Speaker A: 00:06:21

I wanted to be and I, and I want to be, you know, I enjoy any leadership data solutions across the board for an, for an organization. So that is, it's end to end. I don't want to, I don't want to lose grips on a certain portion of the data realm. I can put it that way.

And that's where I wanted to get to.

So I started with bi, started with SQL development and then I was like, I kind of like this but, but, but someone's designing the Solution for me, I want to get to that part. And then I started designing the solutions and I was a data architect, but I was reporting to an enterprise data architect.

And I was like, I want to get to that part because then I can take the whole lot. I can take an organizations.

And then I became that and I was like, well, I still got someone controlling my business, my budget, I want to own that budget. And then I started progressing and getting to where I was getting to and eventually is where I am.

You know, you control the budget, you control the design.

I want to say control, but, you know, I have my fingers in each one of the pies in terms of what does the architecture look like, how are we building that, where are we spending the money? Is that the best solution for the company? Yeah, so.

Speaker C: 00:07:17

So, yeah, so that's interesting. So for you, it was more about having the, the impact and the control over what you were doing, the kind of more strategic aspects of it.

Because I think, I think a lot of people start out and they maybe want to get to that point because it's one, an obvious career route and it just is just moving up the ladder. Or two, it's the, it's the kind of. Because they just think that is the right thing to do.

Speaker A: 00:07:41

Yeah.

Speaker C: 00:07:41

And actually lots of people get to that point.

I mean, funny enough, I was talking to someone this morning who's kind of got to that CTO point was always their big ambition and they get there and they're like, oh, crap, this is like, this is not what I expected to be. Because actually, really what they like doing is the architecture stuff.

They like being in kind of still in the detail, being able to dip in, which gets to become in the cto. Unless you're in kind of a startup or something like that, it's probably quite tough to stay that hands on. So.

But for you, it was more about impact you can have getting involved in the strategy, that side of things.

Speaker A: 00:08:10

Yeah, exactly that. So after Accenture, which is a consultancy, obviously I moved into industry, Right.

So I had my first insurance job in Switzerland for a small insurer that's not there anymore called Glacier. And at that point you start making a difference, right? Because they come in, they say, we've got a problem, we need you to fix the problem.

If you're in a consultancy, you are not the one that goes in and identifies the problem and. And fixes it and builds that solution and then deploys it and then supports it afterwards. You are designing something and then walking away.

That's the Difference with a consultancy.

Speaker C: 00:08:42

Yeah.

Speaker A: 00:08:42

And that's, that's what's taking me essentially back to, to industries for the same reason.

Because with my job, as much as I enjoy data engineering and data architecture and anything that goes with that, including you know, data analytics, if I could just see the end to end solution. That's, that's the passion. Yeah, that's what I enjoy doing. And again, if I, if, if I look at the top end of that, that's the cdo.

So I get to see the end to end solution on, on something that I've or my team has created. Right. What value we've bringing, brought to the, to the, the business. So that's, that's the big thing for me. That's my driver.

Speaker D: 00:09:15

Yeah.

Speaker C: 00:09:16

Okay, so first role in insurance was how long into like how long in, in your career? Because you've, I know you've gone back and forth a bit between consulting and industry and.

Speaker A: 00:09:25

Yeah, exactly, yeah.: 2008

Speaker C: 00:09:30

Okay, so quite, quite early on then. So, so, so what, let's get into that bit a little bit before we kind of talk about your current role and what you're doing at the moment.

Let's get into that kind of consulting and industry bit because I think you, you see lots of people go from one to the other, but you, you, you've gone back and forth a little bit.

So talk to me about what, what you kind of see as the differences, good and bad about each other and what, and why you've, you've kind of now landed, I think in the, in more in industry. And it sounds like that's probably where maybe you want to be, but I, I don't know.

Speaker A: 00:10:01

Well, my rationale for going backwards and forwards and the difference between the two. So the difference between the two, I would say if you're going in the consulting side, you get to see the end to end business. Right.

And that, that's really important. So that, that's the benefit. Right?

You, you're not just building something, you've got an account manager that's asking you how much work is that, how much do you think it's going to cost? How soon can you deliver that for?

You know, if you're an engineer within the consulting side, if you, if you're in industry, you're really focused on your area. I'm focused on data solutions. I'm focused on the governance and the privacy and the engineering, the architecture within the data realm.

I'm not focused on anything else.

Speaker D: 00:10:35

Yeah, right.

Speaker A: 00:10:35

So if I started with industry and just stayed in industry. That's all I would know where moving into consultancy, you get a very broad picture on. On what needs to be delivered.

The reason why I moved, I would say, backwards and forwards between the two. And that was. That was, I would say, personal. I moved a lot. So we moved from the uk, then we moved to, my wife and I, Switzerland.

Then we moved to Bermuda. We moved back from. We moved from Bermuda back to Switzerland, then from Switzerland we moved to. To London.

And all of that was because we made a big move to Bermuda and we had to try to work our way back to get to the uk. And that was the route that was taken. So it was literally we were at an insurer in Bermuda.

The job that was offered essentially was consulting in Europe. Took that for a while and then found the industry back in the uk.

Speaker C: 00:11:27

Bridge, but you got it.

Speaker D: 00:11:28

Yeah, yeah.

Speaker A: 00:11:29

So it wasn't a conscious decision by me going, and when I can move back from insurance into consulting. It was just like. That was the option. I've done it before, so I'll have to do that again for a while and then I'll go back in.

Speaker D: 00:11:39

Yeah.

Speaker C: 00:11:39

Needs much.

Speaker D: 00:11:39

Yeah.

Speaker C: 00:11:40

Okay, cool. So what. Tell me a bit about the Bermuda thing. We kind of. You mean there's. How did that come about and what was it like? Was it good, bad, indifferent?

Like. Tell me about that.

Speaker A: 00:11:51

It was fantastic.

Speaker C: 00:11:52

Bermuda sounds like. It doesn't sound like a bad place to be.

Speaker A: 00:11:55

That's great. So anyone that lives in Bermuda will tell you, you either you're either there for two years or there for 10 years, unfortunately.

Well, unfortunately, we were there for three and a half years, which is just, just, just over. It was purely because of family that.

Speaker C: 00:12:10

That we left.

Speaker A: 00:12:10

My mother and father were in South Africa.

Speaker D: 00:12:12

It's.

Speaker A: 00:12:12

It took about 24 hours, really, to get back. You had to fly from Bermuda to Atlanta and then Atlanta back to Cape Town, and it took, you know, 20 hours at most to get there.

So that's the reason why we left all insurance. I don't know if you. Have you been to Bermuda?

Speaker C: 00:12:25

I haven't, no. No, no, no, no. I've been to other places in the Caribbean.

Speaker A: 00:12:28

It's quite Sur Streets and. And, you know, and beyond. And you'll see most insurers there with a little private, little letter box.

Speaker D: 00:12:36

Yeah.

Speaker A: 00:12:36

As a holding company, but most insurers will have a little presence inside of. So it's a very small lot of expats, I imagine. Loads of expats, but from everywhere.

Speaker D: 00:12:46

Yeah.

Speaker A: 00:12:47

So South Africa, the us, Canada, uk. But That's a great place to live. Yeah, yeah, great place.

Speaker C: 00:12:54

So then you decided to come back and then that. And you went via Switzerland, you say for. In a consulting role. Is it. Which role was that?

Speaker A: 00:13:01

Well, in actual fact. So I say that because we. I moved back to another role which was, I would say somewhat of a. It was somewhat of a demotion and that was what.

That's where I had to, you know, we had to make the decision if we leave now. The only opportunity we had was somewhere in. In Basel. It was somewhat of a demotion from the position I'd had.

So I stayed there for about a year and a half and carried on looking and then found the consulting position while I was there.

Speaker D: 00:13:29

Yeah.

Speaker A: 00:13:29

Okay.

Speaker C: 00:13:31

And so all the, and, and was all the consultant stuff you did, was that all into insurance still? Or was that still. Was that the other side? Okay.

Speaker A: 00:13:39

Yeah, it was across. So when I moved to consulting in Switzerland, it was all banking.

Speaker C: 00:13:46

Okay.

Speaker A: 00:13:47

Although my background prior to that was insurance.

Speaker D: 00:13:50

Yeah.

Speaker A: 00:13:50

Outside of IT and data at an insurer called Athene in Bermuda and Des Moines, Iowa. But yeah, that went into securities and capital markets and then from there moved back to insurance in the uk.

Speaker D: 00:14:02

Yeah. Okay.

Speaker C: 00:14:03

So. So when did the first. When was the first kind of big break into the CDO role then? What did, when did that. When did you get the top gig?

Speaker A: 00:14:12

So I was very lucky enough, I would say, at a young age, because it's young compared to now when I was working at a theme in. In. In Bermuda. I was lucky enough for them to give me the nod, I put it that way for a CDO position at. In the U.S. but again, because of.

It was just bad timing. I love the company. I love where I was living, Bermuda. But because of my family and whatnot, I had to add to say no. So it was a big decision for us.

I mean, literally a promotion on the cards where we were living and then going. I had to take a demotion and move to the eu. So it was a big, big decision from there. I had to work my way back up. Right.

Because once you've got that on your, on your cv, then someone looks at your CV and they go, right, well, what's this blip over here? And you have to try to justify that.

Speaker D: 00:15:01

Yeah.

Speaker A: 00:15:01

So that's when you have to start building yourself up again and, you know, back to essentially where I am now.

Speaker D: 00:15:06

Yeah.

Speaker C: 00:15:06

So when did you, when did you get back? When was the first. First it was the first Chief Data Officer role. The role you're in now after that by title.

Speaker A: 00:15:15

Yeah. So I've been head of Data a few times now, head of Data Analytics. But the actual. But you don't really get a CDO within consulting. Right.

So I was head of data at consultancy called GFT around the corner.

Speaker D: 00:15:27

Yeah.

Speaker A: 00:15:27

And then I was head of data at an actuarial consultancy called Milliman. But again both those don't have a cdo. So I was, I would say equivalent to what a CDO would be but not the cdo and that's, that's what was getting me.

Speaker D: 00:15:39

Yeah.

Speaker A: 00:15:39

And then this opened up again and lucky enough to have that opportunity again. This time I didn't say no because I have to move. Yeah.

Speaker C: 00:15:46

So. So I don't want to go too much into the role.

Now I know you've not been there that long but tell us a little bit about what that role is and what you're doing right now.

Speaker A: 00:15:54

Well, I cover every aspect of data really. So I have a data governance team, data privacy team.

I have data architecture and then a small data insights team that looks at business intelligence and insights. So it's, it's the end to end realm of, of data within the organization.

Speaker D: 00:16:09

Yeah.

Speaker C: 00:16:09

So look as I meant we spoke about it briefly off, off camera but we're, I'm doing a bit of a data series as you know. So what I just wanted to get into a bit is kind of where, very open ended question but where you see kind of data at the moment.

AI is obviously very much in the, in the spotlight. Not, not just insurance but in, in, in in kind of every day off but, but definitely in insurance as well.

People I think just kind of figuring out what they're going to do, playing with, feeding around the edges. The risk, very risk averse industry. Not, not necessarily jumping straight in all in. But where's it at for you?

Like what's the, what's the kind of the big thing on the agenda for you?

Speaker A: 00:16:50

It's the same for everyone, right? I mean AI, AI Gen AI, it's everywhere in everyday life at the moment.

I mean we were saying before it's, it's almost like the replacement of Google at the moment. I mean you know you, you're using a chat GPT instead of Google nowadays to write you something. It's, it's incredible.

I say all of that and it is, it's everywhere within our organization. You know everyone's trying to find that use case where you can I would say somewhat reduce the human in the loop. Right.

Because that's the big thing Nowadays is, you know, you're generating all this, all these insights, but you still need that human to do, to do the checking for you. So I see huge value even in personal life, in work. If anyone knows me, they'll know.

I'll say this because I've been speaking about this for years, and I'll say, and I'll say it again, and I'll say it for another year, is that there's. There's no, there's no AI and there's no, any of it without, without data. Right.

And I think that's, I think that's the roadmap that's coming back from, away from where we were five years ago with the big data and the data swamps, that is data lakes. That happened and everyone was jumping on that bandwagon. We're coming back to data again.

When I say coming back to data, I'm talking about traditional data, structured data, data data that's been modeled so you can gain insights from that. And I say that because if you think about AI, you need trusted data.

You need, and I'm not talking about the gen AI portion of it because that's only as good as whatever you feed it, right. But the training, you need good data to give you good data out, right?

You need input data to give you that good insight and, and that comes from your data estate.

Speaker D: 00:18:20

Yeah.

Speaker A: 00:18:21

So I'm seeing, and I'm even seeing that come more and more, that structured data, the data modeling, the traditional data architecture as such is, I would say, making a comeback, which is good for me.

Speaker C: 00:18:32

When you say going back to it, do you mean as in more of a focus on. And actually kind of, let's get the data that we've got in a good shape and get good practice around that.

Because actually what's going to sit on top of it is all this new kind of clever AI stuff, but actually if the data that underpins it's no good, then.

Speaker A: 00:18:49

Yeah. So 15 years ago, everyone spoke about data warehouses and. Right.

And if you didn't know anything about data warehouses, really you said that they were rubbish and, and they're a failed project. They're a failed project because they were implemented badly. This is back in the day, right.

And then big data came out and, and there was no, you didn't have to know your data before you put it into a data lake. You could just throw everything you wanted inside of a data lake and just put whatever you wanted with no modeling.

And everyone loved it because you've centralized your data. You could never get Your data out, that was the challenge. Right. So it became.

ed it and I think it was like: 2016

If you didn't model it properly, if you didn't actually design your lake properly and your data warehouse properly, it just became, you know, useless, basically.

Speaker D: 00:19:34

Yeah.

Speaker A: 00:19:35

So what are people doing nowadays? I mean, besides the whole discussion around data mesh and democratized architectures and data products, I forget about all of that.

People are regardless taking that data and modeling it. They're taking out the nuggets of information and then modeling it.

However, which way we want to talk about modeling whether it's, you know, relational or whether it's Y tables or whatever it is, they are modeling it now. So they'll take it out and do something with it and create an input to something. Right.

Because that's how you trust your data because it's structured.

Speaker D: 00:20:03

Right.

Speaker A: 00:20:03

So you have to be able to look at your data in a certain format. Everything essentially when it comes into a report then is somewhat of a structure.

Speaker D: 00:20:10

Yeah.

Speaker A: 00:20:11

So that's where we're going back to is that we had full, too much rigid structure to start with. We then moved to, you know, we don't, we don't want that anymore. Let's put everything inside of one single location.

And that's where you get your, your, your lake. And now it's gotten to the point where, well, I can't get anything out of that.

Speaker C: 00:20:27

Right.

Speaker A: 00:20:27

So how do we go back to what we had but not make the mistakes of the past? And that's where you're getting these strips of value that you get from these, I would say, lakes, as it were, from the past.

Speaker C: 00:20:39

And is that, is that being driven by the kind of realization that in order to use AI and the benefits of that effectively, that stuff needs to be in good shape. Is that, is that kind of where. What's driving it back to that point?

Speaker A: 00:20:55

That's one of the reasons, I wouldn't say again, when I talk about. I'm talking and I'm talking about the trusted input data. I'm talking about, I would say traditional AI nowadays and ML. Right.

As opposed to gen AI as such. I would say that's one of the aspects that is a.

To gain any insight from your data, regardless of if it's, you know, predictive analytics in ML to gain any insights, you have to trust your data.

Speaker D: 00:21:16

Yeah.

Speaker A: 00:21:17

So even a report, forget about ML or AI, to generate any kind of reg reports, it needs to come from a trusted Source the regulators will come along and look at your report and say where'd your data come from? Yeah, and you'll have to prove it. If you say it's come from that swamp over there and they go well where inside? Then you go I'm just not sure.

Yeah, you can't use it. So that's where it's coming from is that without creating that structure which is forced again by regulation, it's useless.

Speaker C: 00:21:43

Where do you think insurance as an industry are on, on that?

Like, I mean I know there's the kind of age old thing where people would say it's like kind of really behind the times of that but like from a, from a data specific perspective from what you've seen in, in your consulting days and I guess more recently where, where is, is an insurance doing okay on that or is it, is it.

Speaker A: 00:22:04

I think it varies. I think it varies. It's not. It's the same as banking to be honest.

Speaker C: 00:22:08

Okay.

Speaker A: 00:22:09

It, I say it's the same as bank. It's the same as banking in that some banks are less mature than others, some are far more mature than others. And that's the same with insurance.

Some insurers haven't even gotten to that discussion of data warehouse, data lake, democratized architect and data mesh yet. I mean some insurers are still talking about individual data silos on files where some insurers are now fully invested into even Gen AI.

I had a meeting the other day with a few other CDOs and they were embedding Genai throughout the organization. I mean and they were. And usually there's a certain degree of fear without having that human in the loop.

But I mean embedding it through documentation, looking through everything, all legal documentation, generating documents, I mean that's a big step, right? Trusting everything that comes out of those tools that you're putting in place. But it depends. I'd say some are and some aren't.

Speaker C: 00:23:07

That's, it's an interesting way because actually I, I actually put a comment on, I think someone from Capgemini that put something out the other day about AI and, and the question I kind of posed was where are generally, I mean what, what I wanted to know is what that, what the, in what he saw as the industry where they were at with regards to kind of Geni Gen AI and stuff like that. Are people still just kind of playing around with it?

And just because I mean I went to an a diner and exec dining event probably seven or eight months ago and at that point Everyone was playing around with it but the general consensus was we're just kind of playing around with it for a few guys in the corner and we kind of ring fence something and they're just figuring it out really with co pilot and so on and so forth. But, but no, no, it hadn't reached the point where people have put it into kind of production and actually using it. But it, it sounds like you get.

We're getting to that point.

Speaker A: 00:23:54

Oh yeah, yeah.

I mean the one use case that I'm seeing and I'm hearing from, from, from everyone, even the consultancies are doing it and even some of the consultancies that are doing it are very impressive themselves that they, they've done this. But everyone is. It's document analysis, right? Yeah, it's, it's looking at, for instance, if you look at insurance like claims adjuster notes.

Speaker D: 00:24:12

Right.

Speaker A: 00:24:12

And pulling out nuggets of information from you know, free handwritten notes and pulling out and summarizing those notes, that, that is happening a lot.

If we look at acquisition data, data that comes from third parties and we have documents and we want to analyze those documents instead of having, you know, analysts read through the document and summarize them, you can use Genai to summarize that, that kind of, kind of information and that's, that's being done all over. And the consultancies, all of them have a tool for it.

Speaker D: 00:24:39

Yeah, yeah.

Speaker A: 00:24:40

But the, the insurers are doing it too. And that takes you to the next step.

Speaker D: 00:24:42

Right.

Speaker A: 00:24:42

Because even Microsoft and I embedding it within copilots, I mean that's, that just takes it to. So all this money that everyone's been investing for the past year in Gen AI and how to analyze your documents.

It's, it's, it's coming from the vendors, I don't want to say for free, but it's coming from the vendors, you know, at AS is. So yeah.

Speaker C: 00:25:00

Relatively cheap. I mean, I guess it's an interesting one you make about legal, I mean I can imagine legal documents and stuff like that.

There's a massive time saving there. You've got people reading all through the, the detail there. If you can get AI just reading through that and analyzing documents.

But it's just how much you trust it.

Speaker A: 00:25:14

Right, that's exactly it. Yeah.

Speaker C: 00:25:16

And do you see their evolution there being in the sense that actually. Right.

We try it with this and if nothing falls over and there aren't any major, I mean, I guess businesses will factor in an element of risk there and think Whether it's worth it or not, like they do with everything. But that works. Nothing major happens. There's no major problems.

And that's the precursor to moving on to kind of bigger and better things to see that as how it goes.

Speaker A: 00:25:43

I imagine it will, I can put it that way. I mean, let's put this way. If I was asked right now, we have a tool, analyzes all these documents. It's been correct every time for the past month.

Yeah, I'd go, I would still want a governance team to oversee it and put out because if, because everything, if anything happens.

Speaker D: 00:26:03

Yeah.

Speaker A: 00:26:03

I'm going to Bob in legal and I'm going to say, why did you let that happen? I'm not going to the AI solution on a desktop to say, why did.

Speaker C: 00:26:12

I hold that to account?

Speaker A: 00:26:13

No, no. So when the regulators come up to me and say, what the hell? Why did you do this? I can go, I can add an audit trail on how. Right.

So unless the regulators tell, fine, you can steamroll ahead with your AI solutions and then fine. But I mean, I wouldn't put my head in the block for it now just because I need accountability.

Speaker C: 00:26:30

Right, yeah, but that to me is the biggest hurdle for that. Again, I'm certainly no data expert, I'm not a techie at all.

But just from a kind of layman's perspective and looking at how businesses use that, it's that final kind of massive hurdle about trust, isn't it? Like, you mean, can, can you trust it?

And, and it's, and it's great when we're even talking off camera about me doing a story for my daughter on chat GPT. I mean, look, there's, there's no, there's no jeopardy there. So we can create anything apart from it could put something pretty crazy there.

But I'm still reading it. So. I mean, I can always change something on the, on the, on the, off the cuff if I need to.

But like the, but when, if, I mean, when does that point get to, to where you actually trust it, that you allow it to everything like you say, and you put your neck on the line?

Speaker A: 00:27:16

I suppose if you really, I mean, I've never really thought about it, but I suppose if you really think about it from my point of view, it's when the regulators come along and say, we're happy for you to do that. Yeah, yeah, yeah, right. As soon as they say that, then there's no argument from me. Then it's, then it's anecdotal. Right.

Then it's feeling Based, Right. It's me going, I don't feel good about this.

Yeah but if they are saying you can do whatever you want, you can go away and create your AI solutions if you want, you can create whatever insights you want but we are not going to blame the machine, we're going to blame you.

Speaker D: 00:27:44

Yeah, right.

Speaker A: 00:27:45

But as the day they come to me and say you can, you can, you know, we've tested this, we understand this. If you're using these principles and then you can go away and that's fine.

Speaker D: 00:27:54

Yeah.

Speaker A: 00:27:55

Then that's the day I'll go fine.

Speaker C: 00:27:57

Yeah, we said and at that point your next actually not on the line.

Speaker A: 00:27:59

You got it? Yeah, yeah.

Speaker C: 00:28:00

What, what, how do you, how long do you think that's that is away?

Speaker A: 00:28:05

Oh, I don't know, I don't know.

Speaker C: 00:28:07

Long time or, or not as long.

Speaker A: 00:28:09

As we, it's reducing, I would say it's reducing. Right. There's got to be a certain degree of trust. But I say I'm, I'm, honestly I'm, I'm not sure, I'm not sure that that's a massive move. Right.

Speaker C: 00:28:21

But that's the thing, I just can't like for me you mean. Look again, I've never ever dealt with a regulator. I can only base this on what I read in just general industry stress.

But they're obviously very risk averse given their position. Logic would tell you something.

I mean I don't know what the tipping point is to that like you mean and given that they, they're not a business so there's no money saving directly for them. It's just, it all just benefits the market unless they get loads of pressure put on them to get to that point.

I mean I just don't know what the tipping point is. I can't get my head around when that might happen. Whether is it months, years, like I.

Speaker A: 00:28:55

Just think it needs to be, it needs to be throughout the organization.

Everyone needs to be using it first and more organizations need to be using it first so it becomes, it gets to the point where it's, it's self proven at the moment.

Like I said, a lot of, you know, a lot of companies are using it for generating, you know, analyzing and documents giving a bit of insights out of there. I mean, I mean no regulator's gonna go well that's great right? You've done an MVP for the past six months.

Yeah, let's go ahead and ignore all of the regulators regulations we put in place. But when everyone I would say is when it's fully embedded within every organization.

Not every, but you know, it becomes more mainstream then it becomes, I feel, more of a discussion point, more of a decision point.

Speaker D: 00:29:39

Yeah.

Speaker A: 00:29:40

As opposed to how could they right now. Yeah, how could they do it right now with as much as, you know, you like writing stories for, for, you know, your daughter at bedtime.

And I'll do the same thing and if I don't like the story, I'll update and say no, change that to, you know, that, that dress color to red and. Yeah, but you know, that's, I don't know that's. I see that being a while.

Speaker C: 00:29:59

Have you seen any kind of proof of concepts or anything like that in insurance?

Because you mean the stuff we're talking about at the moment around kind of document management and analysis and all that kind of stuff is really just an adaptation of what we're using it for in our kind of personalized data and stuff. It's just that there's just more, there's more at stake for that.

But are you seeing anything in the industry that's particularly interesting about how that really pushes the needle onto the kind of savings real serious time and, and, and, and kind of revolutionizing like user.

Because that, that's the thing for me is like, it, like from a customer perspective, like there has to be some, some user experience, customer experience, benefits that are potential. But how long far away that potentially is.

Speaker A: 00:30:43

Yeah, I've heard, I've heard other insurers doing that, focusing on cx, on customer experience and, and, and whatnot.

I think Game Changer that's coming up that, that starts well, it's here already, but I wouldn't say it's just not automated yet is the automation of coding. I mean everyone knows that you can, you can generate code. My son's 14.

He sits in class with a, with a version of ChatGPT which the school gives him to generate python code. He's 14 and he's writing Python at home and he calls me upstairs to help him and then he uses his Gen AI solution to help him with the syntax errors.

I mean it's, it's, it's mental. Right? That's where I see, that's where I see it going again.

I've seen before where the consultancies have made a tool out of this, where it generates code for you and generated documents for you, which is fine a year ago. And that's how fast the industry is moving because that's everywhere now. Generating code, generating a data model.

I mean back in the day I used to pride myself, this is 15 years ago.

I used to pride myself because I knew data modeling really well and I could argue you can go in now to, you know these tools and just say create a data model with this, give it the whole data set. So there's a schema, one big white data set and said create me a relational data model that'll work for X and it'll do it for you.

That's where I see in terms of it anyway. Massive cost saving is generation of. It's a thought leadership.

Speaker D: 00:32:06

Right.

Speaker A: 00:32:06

It's not just documentation data models, data engineering. I want to write this in R now I want to write it in Python and then changing all your transformation processes.

That's where I see huge, huge savings going forward. If I could. It's not, it's not 100% there yet obviously, but once that becomes mainstream again.

Speaker C: 00:32:25

That'S a huge game changer.

Speaker A: 00:32:26

Yeah. Suddenly data engineering is, is prompt engineering.

Speaker D: 00:32:30

Right?

Speaker A: 00:32:30

Asking the right questions for. In the right language.

Speaker D: 00:32:33

Yeah.

Speaker B: 00:32:36

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Speaker C: 00:33:51

I mean that's a really good point. Excuse, you mean.

Look, obviously I'm not in it, but I, I used chat GPT as I'm trying to use it as much as possible but it's, it's a lot of it is like kind of knowing what it can and can't do and what therefore what you, you prompt it with like, I mean some of the basic stuff's pretty obvious but, but it's. I spent quite a lot of time actually just reading about prompts to put in. And, and I mean one, one of the things.

I don't know if you've ever seen it, but there's a, there's a thing you can use now. It's like an automated meeting taste called Fathom. But you use it on like teams calls and it. Minutes you meet and I mean it's, it, it's.

I'm yet to find any mistakes in it. It's unbelievably good. You can now ask it at the end, like critique how I did in that meeting. It will tell you if you spoke to us.

So, so, but, but again you have. They had to tell me what to type in there, so I would never have known what the prompts are. But that, that's part of the, the learning.

Is it like learning what prompts are? Like you said, like if you, when you can figure out what you need to put in the machine to get that answer out, then suddenly. And I think people are.

This pace of learning for that is.

Speaker A: 00:34:57

Is.

Speaker C: 00:34:58

Is. Is Gavin Government serious momentum.

Speaker A: 00:35:01

Yeah. Yeah. So mama is another case of that.

So my wife's friend, I have no idea how this has come about, but she's doing, she's doing a degree in, in some form of nursing and I don't know why part of her coursework is R for she has to do some kind of statistics in nursing. But regardless, she sends. My wife sends me a WhatsApp message the other day and it's literally a screenshot of her friend's R code.

Speaker D: 00:35:30

Yeah.

Speaker A: 00:35:31

And can't figure out what it is. So I read the first couple of lines. I kind of make out what it's doing, but I can't make out the last one.

I literally just took the image put into chat GPT and said what's the error here? And it sends back the, the message saying the area. And I said just put this in plain English types. That tells me that the error.

I just copy that, send it to my wife said there you go. Tell her what it is. But that's, that's it. I mean that you don't have to understand what syntax area is. You can literally just type it in chat GPT.

What's the problem with this? Tells you exactly in plain English what's wrong with your code. And it says this is a. These are possible solutions on how you can change your code.

Speaker C: 00:36:02

Yeah, fascinating. I mean I could talk about it all day, but. But yeah, Look, I mean, it's obviously going to be. It's going to be an interesting 12 months.

Certainly like where we are in the next year, I think, is the pace of changes. It's going to be unbelievable, isn't it? But let's go back into. So going back to the. The care side of things.

Like what One of the things I always like to. To get from someone is, is the, the really good things that happened and some of the failures that happened. Don't focus on the failures too much.

But, but there normally is some throughout your career. What, what are the. What would you say are the kind of big landmark things that happen, whether it be jobs and.

I mean, we touched a little bit on the turning down a role and stuff like that, but. And, and just some kind of learnings and advice that you've taken from or advice you'd give to other people after, after off the back of them.

And then, and then maybe we can go into some of the things that maybe didn't go quite so well.

Speaker A: 00:36:54

So what's the first part? My key roles?

Speaker C: 00:36:56

Yeah. Just.

You mean, what, what would you say the kind of, biggest kind of successes you've had, the big kind of achievements and like the landmark things in your career?

Speaker A: 00:37:05

I guess I would say landmark. And I've thought about this. The butterfly effect. Right. If you didn't do that, would this have happened?

Speaker C: 00:37:10

Yeah.

Speaker A: 00:37:12

And I would say the number one key would be my very first job in the uk. So I was. I mean, I came over here, I came to the UK with £70 in my pocket.

Speaker C: 00:37:22

Wow.

Speaker A: 00:37:22

I said, it's. My sister had a place, so I stayed at her place. And I mean. And she was, she was practically, you know, you need to get out eventually.

I mean, after a couple of months, night. This is. Back in the day, we didn't have WI fi.

I used to have to go sit at an Internet cafe eight hours a day, applying to every single job I could find. I don't care what it was, anything in I t. And I was sitting there, applying, applying for work.

And eventually I was interviewed by Peter Daly from Accenture, and I'll call him out because he is the one that started my career. So he gave me the first, my very first job at Accenture.

Speaker C: 00:37:51

Is he still there?

Speaker A: 00:37:52

I think he is, yeah. He's a partner there now. But. But he's the one that approved me. He's the one that said yes. And that was the key milestone.

Speaker D: 00:37:58

Yeah.

Speaker A: 00:37:59

Because without that, again, I thought about this with the butterfly effect. Right.

Speaker D: 00:38:02

Yeah.

Speaker A: 00:38:02

If I didn't. If I didn't take that role. And they offered me to. To transfer to Warwick.

Speaker D: 00:38:09

Yeah.

Speaker A: 00:38:09

By another partner called Steve Randall from Accenture. He's the one that moved me because he asked me if I wanted to. And in Warwick's way, I met my wife and had kids.

Speaker C: 00:38:16

Oh, wow.

Speaker A: 00:38:17

Yeah. So all of that just from. So, so did you.

Speaker C: 00:38:21

You were based in Warwick?

Speaker A: 00:38:22

I was. Second time around. Yeah. Initially I was based in London in Chancery Lane.

Speaker D: 00:38:26

Yeah.

Speaker A: 00:38:27

And then the, the, the move.

Speaker D: 00:38:29

The.

Speaker A: 00:38:29

The project moved to Warwick and they asked me, do you want to. As an option? And I said, yeah. I mean, I was a bit of.

Speaker C: 00:38:37

A random place to go when you've been in London.

Speaker D: 00:38:38

Yeah.

Speaker A: 00:38:38

I had no ties or anything, so I was like you. Yeah, fine. So I moved to. I moved to Warwick. I didn't even have a car. I used to cycle to work in. On, on dual carriageways. I mean, it was horrendous.

Yeah, but, but, yeah, so that was, that was my one key. The next key, I would say was the move. My first move to Switzerland.

Speaker C: 00:38:58

Why did you do that then? Because we touched a bit on the Bermuda thing. But why did you get. Because you went to Switzerland, then Bermuda.

Speaker A: 00:39:03

Right, you got it.

Speaker D: 00:39:04

Yeah, yeah.

Speaker C: 00:39:04

So why did you go to Switzerland in the first place?

Speaker A: 00:39:06

Right before the: 2007

Speaker C: 00:39:09

Okay.

Speaker A: 00:39:10

Yeah. So right before that, there was a 2P happening at the organization that I was at. You had to move over to.

They offered you to move to the, the new consultancy. It was good timing. This job came up in Switzerland. It was. It was a data architect position. I wasn't a data position already, but it was.

I think it was taking my career towards bfsi. So at that point I wasn't in BFSI at all in the UK and that was. Offered me a position at an asset manager.

Speaker C: 00:39:36

Right.

Speaker A: 00:39:37

And I figured it was a good move just to move to Switzerland, get that experience within banking.

That's another key milestone for me because from there suddenly my CV was banking, which is the direction I wanted to go within BFSI and data engineering. With, with architecture. Those are two big key. And then the next one would have been. Bermuda was the pinnacle for me that, that really set me up to.

I would say Excel within. Within Europe.

Speaker D: 00:40:02

Yeah, yeah.

Speaker A: 00:40:03

Whereabouts did you go in Switzerland initially was Zurich. I was there to start with for a couple of years.

Speaker C: 00:40:09

Amazing place, Zurich, isn't it?

Speaker A: 00:40:10

Yeah, it's great.

Speaker D: 00:40:11

Yeah.

Speaker A: 00:40:11

But I preferred. The second time we went back, I actually preferred Basel.

Speaker D: 00:40:15

Really? Yeah.

Speaker C: 00:40:16

I've never been my One of my best friends from university's dad worked for Zurich, and therefore when he finished uni, he went and lived over. So I went over there a few times, but it was. Yeah, it was a.

Speaker D: 00:40:25

It was a.

Speaker C: 00:40:25

It was a cool place.

Speaker A: 00:40:26

But they're both very cool. Yeah.

Speaker C: 00:40:28

Yeah. So that must have been quite an interesting time. You went over there just to work for an asset manager, just as a bank.

I mean, that was literally as I started in my. My recruitment career as well. Not the great time to. To start. Start doing. Doing that job when everyone was firing and not hiring.

Speaker A: 00:40:42

That was terrible.

Speaker D: 00:40:43

Right, yeah.

Speaker C: 00:40:43

So what was that, what was that like working in banking in. In that time?

Speaker A: 00:40:48

It was fine to start. It was almost like Switzerland came last.

Speaker D: 00:40:52

Yeah.

Speaker A: 00:40:52

So we heard about everything that was happening around the world, obviously. And then was it about six months into it, that's when the asset manager also went, right, we're cutting. Yeah, it was horrendous.

Speaker D: 00:41:04

Yeah, yeah.

Speaker A: 00:41:05

We sat in the office and we literally saw managers getting up. They had this little meeting room with one single senior manager inside of it, and everyone in the office knew that if you walked into that room.

Speaker D: 00:41:17

Yeah.

Speaker A: 00:41:17

You were walking out the office.

Speaker D: 00:41:18

Yeah, yeah.

Speaker A: 00:41:19

And that's what was happening. And we were all sitting there and we were watching people go in that room and just walk out the office. Go in that room and walk out the office.

At one point, the one. The one manager, we saw him get called and he just got up and left the. He didn't even go speak to them. It was horrible.

I mean, you saw all these people just losing their jobs over and over. I was a contractor, so they got rid of very little contractors, but they just reduced everyone's rate by like 20 or whatever it was.

Speaker D: 00:41:43

Yeah, yeah, okay.

Speaker A: 00:41:44

But, yeah, and then I left, then went to an insurer. That was my first insurance gig straight after that.

Speaker D: 00:41:50

Yeah.

Speaker C: 00:41:50

So it seems to me that the kind of. The big.

I mean, you correct me if I'm wrong, but the big learning from that, and this is a fairly common theme I've done a lot of these podcasts now, is kind of taking a risk.

And like, when an opportunity presents itself, whether it be Warwick, Switzerland, Bermuda, except like, and there's probably other mini risks within them as well. But the people that always seem to got on, go on, have 10 generally taken a couple of risks like that. They've jumped into something when they.

When there was an opportunity there. And. And they've got a bit of variety. Like, would you.

Would you put them down as like, kind of like, if we, if we're trying to scale that down to advice that you would give someone else in that scenario is, is if I kind of.

Speaker A: 00:42:35

That's exactly. It is about taking a risk. It's about, it's about also broadening your search. Right.

I find, you know, people that stay within a certain, I would say region and work within, you know, a single, I don't want to say a single company.

I mean, you can excel within, within one organization, but if you want to grow and you don't feel like you're growing where you are, there are other options is the point.

Speaker D: 00:42:57

Right.

Speaker A: 00:42:57

And those options are you can broaden your search, take the risk and go somewhere else. Right. Whether it's a country, one organization, or a different industry altogether, nothing's stopping you as an individual from progressing.

You know, no organization can define your progression. If you want to become a CIO or CTO or cfo, that's up to you, not the organization where you're at. Right.

If they're not giving you that opportunity and the region, if there's no opportunity in your region to become whatever, the chief architect.

Speaker D: 00:43:24

Yeah.

Speaker A: 00:43:25

There is somewhere in the world that is, there's somewhere in the world that will offer that to you.

Speaker D: 00:43:29

Yeah.

Speaker A: 00:43:30

So it is just about broadening your search and taking a risk elsewhere.

Speaker D: 00:43:32

Yeah.

Speaker C: 00:43:33

So. So I think we've got some, some good snippets in there. What, what about the, the bad stuff.

Any, any particular failures or negative things that happened and like, and I guess I'm more. So I'm interested in the kind of the learnings that you took away from them.

Speaker A: 00:43:48

Yeah.

You know, my wife and I talk about it all the time, but everything's worked out for the best because we are in a really good place, you know, with the, with the, where I am in, in my career and personally whatnot. But you know, moving from Bermuda was hard. Taking it and, you know, essentially turning down a, a senior level position was. Was very hard.

And then moving and then getting demoted to another position was, was, was like a double whammy. Yeah. Yeah. So I mean that's, that wasn't, that wasn't fun.

And then my parents by the way, left South Africa and moved to the UK within about two years of, of this all happening. So we didn't actually have to. Yeah.

Speaker C: 00:44:26

Does that still get brought up? I'm not sure if my dad for.

Speaker A: 00:44:32

That, but what are my learnings from? So I would say I came back from that.

Speaker D: 00:44:38

Right.

Speaker A: 00:44:38

So once I took that position, I knew that I had to I wouldn't be able to apply for a straight out CDO position.

Speaker D: 00:44:45

Yeah.

Speaker A: 00:44:45

I knew I had to start somewhere else and I knew I had the consulting background. So how can I get back into consulting and work my way back up like I did before? Getting back up.

Speaker C: 00:44:53

And that must have been really tough given that you like you were basically at the, at the point you get that and you've kind of almost potentially put your back self back.

Speaker A: 00:45:00

Yeah. Four or five years it screwed in my head. But then, you know, honestly you pick yourself back up.

Speaker D: 00:45:07

Right.

Speaker A: 00:45:07

So that's where I figured, well, what, what can I do to get to where I was before? It's going to be difficult doing that whole route again.

hen it became a thing. Right.: 2016

And I took a, I did a master's in, in it was information management, but it was a focus on big data and principal analytics just so I could have the paper. It wasn't because. It wasn't because I wanted, I didn't have a massive interest in big data. Yeah.

I wanted the paper so that I could have that credential behind me for my next position. And the next position was, was consulting and you know, the rest is history. Consulting, sales, where I am, where I am now.

Speaker D: 00:45:50

So. Yeah. Yeah.

Speaker C: 00:45:51

Did you. I mean the bit that strikes me again there to kind of picking that apart of like the kind of.

The learning or the piece of advice there is is that as much as you had to take a step back, it sounds like you always had the plan.

Like there was always like for everything you've done, it seems like there was a strategy and the line's not always straight to get to it in your case, definitely not.

Like there's been, there's been some bumps in the road but, but ultimately you've got to where you need to because you had a plan and you strategically looked at moves and stuff like that. Do you think, do you think. Because I think a lot of people underestimate how important that is. And, and maybe in the early.

And obviously I speak to people about careers and jobs like day and day out.

Speaker D: 00:46:30

Right.

Speaker C: 00:46:30

But in the early days I think it's kind of fine to like float around a bit.

But it, it kind of always does amaze me how many people say they want to be a CIO and, and when I Ask them, like, well, kind of, what do you want to do for your next role? And.

And they don't really know, or there isn't necessarily a kind of a plan, or they don't understand where the gaps might be in their experience that they need to fill in order to get that right. It. You've always had an awareness of what, what that is and at least had an educated guess at how you. How you get to that point.

So do you think that's important for people to. To do that?

Speaker A: 00:47:04

You know, I started by saying I always had a plan to be a cdo. I didn't have a plan to be. To grab the title cdo. I had a. I always wanted to become the head of Data, whatever that is.

Speaker D: 00:47:15

Right.

Speaker A: 00:47:15

So that. That is owning the Data estate. And I've always wanted to do that. But I say I always wanted to.

It's because once I started the data work, I enjoyed it so much and I genuinely did. You enjoyed so much? That's all I've ever done. I just wanted the next step.

And that's where, right in the beginning you start looking, well, the next steps, you know, the senior parts. And the next step after that's designing. And then you're like, well, the person. Now I see the person right at the top.

Well, he's controlling all of this and he has a. An insight into all of this. I want, I want that. And that's where I was aiming to.

Speaker D: 00:47:47

That.

Speaker A: 00:47:47

That was my. And you're right, it's the gaps, right? It's. It's the understanding that when I was an engineer, I mean, like you said, I'm just fluttering about.

And so they offered me a job and work, like, yeah, I'll just do the same thing in Warrior and just carry on doing it. Right. But it's when you get to.

Once you become that designer and you're like, I want to go buy this piece of tech, and someone says, there's no budget for that. And you're like, well, hold on a second. So what is the budget? How do I figure out what the budget is? Who do I speak to about that?

And then I got my manager who's meeting up with the vendors, and they're making decisions like, no, but I'm writing this and I'm designing this. Why is he doing it? Why is he managing that?

Speaker D: 00:48:19

Yeah.

Speaker A: 00:48:19

And that's where I started thinking, well, I want to get to that. I want to make that decision so that my designer can design based on a good decision that can. Then the builder can then build on the. And it's.

I always have a plan based on, I would say my trajectory. And I think, I think a summary of that is. Is having the passion for the work.

Speaker D: 00:48:38

Yeah.

Speaker A: 00:48:39

Right. I've. I love what I do. So getting there was all. Because I enjoyed what I do. It's. I wasn't aiming for. It's like me saying I want to be CEO.

Speaker D: 00:48:49

Yeah.

Speaker A: 00:48:50

I have no. There's no ambition there because I don't want to run an organization. I just want to run the data portion of it because that's what I enjoy.

Speaker D: 00:48:57

Yeah. Yeah.

Speaker C: 00:48:58

So. So that, that's a really good, good point actually.

That's that leaning into what you're good at and then, and then kind of that inquisitive nature around kind of where that takes you and what you actually want to be doing because you enjoy it. And that's kind of what I said when I said earlier on. I think lots of people get. Get their head channeled down on.

They want to be a CTO or they want to be a cio. They get there and they're like, actually I'm not writing code anymore or I'm not doing kind of architecture or whatever it might be.

I'm not actually doing delivery. I see that all the time. The amount of people that go from a kind of program director into a kind of a head of transformation.

So I'm actually not doing delivery stuff and that's what I'm great at. Kind of lean into what you're good at. Yeah, I think that's. There's some definite kind of learnings there. And, and also the going to.

I think you, maybe you, maybe you don't underestimate it. But like the, the pushing yourself to kind of go to Warwick and stuff like that. Like that.

That to me opens up the fact that you're prepared to go to a different place. If that hadn't happened, would you have been open to going to.

Speaker A: 00:49:56

Exactly.

Speaker C: 00:49:57

To Switzerland or Bermuda. But you mean. I suppose you'd always come. Already come from South Africa, so you'd already done a big move. It's like bigger than going to Warwick. But.

But yeah, that to me I think that that risk taking thing is. Is in everyone I've interviewed that there's always some common. That's a common thing. I think people seem to have taken a risk.

Speaker A: 00:50:16

Yeah. Yeah, that's true.

Speaker C: 00:50:17

So I just wanted to kind of move on to kind of looking a bit more around the future now then we've obviously touched a little bit on on AI, but kind of more specifically kind of broader insurance with technology.

What do you think of the, the kind of the, the big things that over the next kind of couple of years that how, how insurance is going to kind of evolve? Not maybe, not necessarily. Maybe it's kind of AI center of everything at the moment, but, but, but kind of more broadly.

Speaker A: 00:50:44

I mean, I won't talk about insurance specifically. I can talk about broader in terms of BFSI completely.

I mean it's, it goes back to what I said before, around, around the, the data estate and moving to, moving to where we are now. Right.

If you look at the evolution of where we come from, where I mentioned before on data warehousing and data lakes and then last year and the year before, everyone's talking about the democratized architecture. We are moving back to I would say central views of, of data.

When I say central views of data and this, this is included in, in, in insurance rights, reinsurance, whatever. We can't work in silos anymore.

So what's, what's come about with the likes of democratized types of architectures and, and having a solution per business domain.

Speaker C: 00:51:25

Right.

Speaker A: 00:51:26

Is that we create these silos, we create these walls between business departments where you know, we're not sharing data. We don't know what's happening between teams.

We're not, we're not looking at gaining insights across an organization as opposed to insights from just the business domain. I'm seeing a move towards, I don't want to say centralization like it was 15 years ago, but centralization of results.

Speaker D: 00:51:48

Yeah.

Speaker A: 00:51:49

So what is actuarial? What are they, what output are they developing and centralizing that? What are we getting from the financing? What does the balance sheet look like?

Well, we can centralize that. What are we getting from the, the claims teams? What we can centralize that and what are the common denominators between each one of those?

So we can look at a shared view on all that data. That's where I see we're going back to. That may seem, when I say that it may seem like obvious that, that that's we need to get to. It's not.

We've moved away from that.

Nowadays it's all about the democratized architecture where we have, you know, claims has their view of data, actuarial has their view of data, finances, their view of data. Trying to finance is treating master data differently to, to claims and therefore they have two different versions of the truth.

And when I can say finance may be looking at currency and calling it gb, where Claims is looking at it and calling gbp and then as soon as you run a report, you haven't got the same fields across both of them. And that's all because we don't have a central view, a mastered view of data across domains.

And that's not where I am necessarily at the moment, but I'm seeing that across industry everyone has moved from failed centralized solutions to democratize.

Well, to data lakes, which is dumping everything in there, to a democratized architecture where claims does their own thing and you know, someone else does their own. And now we're moving away to again, how do we bring that back and centralize?

And I'm seeing that not just in insurance, there's a few big banks out there that are going back to that model and bringing it back into a centralized location.

Speaker C: 00:53:16

As in. So kind of once you get that kind of central source of truth, then everybody's working from the kind of singing from the same hymn sheets, I think.

And then you can start to kind of build more. You got stuff on top of it. Yeah, that makes sense.

Speaker A: 00:53:28

Yeah, I'd say, I'd say the difference between the NOW version because you'll, you know, people will hear this and completely disagree. You know, people will hear this and go that, that's the data warehouse is data. We don't. That's not what I'm talking about.

I'm not talking about the individual single source of truth for data warehousing. I'm talking about you keep you descent, your democratization.

You let claims will continue working as they are, Finance will continue working as they are under a certain degree of governance. It's the results from each, it's the shared dimensions from each that are centralized.

That's, that's the direction that I think we're going back to as opposed to just centralizing or just democratization. We're looking at stripes that creates a result store. We put it that way.

Speaker D: 00:54:10

Yeah.

Speaker C: 00:54:10

Okay. And then, and then I wanted to have a just touch briefly on the, the kind of role of a CDO and how it's seen in insurance business now.

Because you mean there's been lots of discussion over, over the years and I've certainly spoke to people about it around the kind of CIO role in the sense that you get lots, see lots of CIOs reporting into a COO, so therefore don't necessarily have a seat at the, the kind of table into the CEO. I think that's kind of changing it. You mean it certainly, in my view, it certainly should be. I mean Tech is the. Obviously the future.

But with AI now becoming a massive talking point and therefore data. You mean, I. It's always kind of blown my mind a little bit.

The insurance companies aren't that great with data given that's exactly how they price stuff and everything. It's kind of that the opportunities are endless really like for. For competitive advantage and yeah. Money making. Etc.

Are you seeing more of the CDO role therefore becoming more prominent? It certainly seems to have moved more outside of. You don't get as many CDOs reporting into CIOs now. Which. Which probably used to happen.

Speaker A: 00:55:14

Yeah, you pretty much nailed it. That's what's happening. Happening. Right. The CD CDO or the CDAO is. It's becoming an office on its own.

So you know where before it was, it was part of the. The IT office.

Speaker D: 00:55:27

Yeah.

Speaker A: 00:55:27

It's now becoming a part of its own office. Right. Data is not seen as tech and tech is seen as your system and your application architecture.

Speaker D: 00:55:33

Right.

Speaker A: 00:55:34

It's seen as your, your business function, your business applications where the data is seen as the, the solutions that hook that all together.

Speaker D: 00:55:41

Yeah.

Speaker A: 00:55:41

Right. The value that you can get from all those different systems.

But 100%, you can see it literally the divide's happening at the moment where you have data which isn't part of tech anymore, even though it's still tech and then you have it. So the two different offices.

Speaker D: 00:55:54

Yeah, yeah.

Speaker C: 00:55:55

I've certainly seen a lot on like LinkedIn and.

You mean certain people waiting in, in the data space kind of thing saying that like finally kind of data is not seen as tech because I guess there's a. There's an element of data that is technology, but there's also a big element of data that's not just directly tech.

Speaker A: 00:56:09

It depends how you separate it. Right. And I think, and there is a debate around that, you know, tech being again business application.

It's the infrastructure that, that it's based on. It's. It's the point to point solutions for the system which you can still go away and design.

But then the data portion comes in where you have your systems. But it's the, it's the flow between them.

Speaker D: 00:56:27

Right.

Speaker A: 00:56:28

It's a centralization of them. It's the, it's the insight you can gain from that whole solution that's already in place on the infrastructure. All those applications.

How can you add value to that?

Speaker D: 00:56:37

Yeah.

Speaker A: 00:56:37

Is the data office. Right.

Speaker C: 00:56:38

Do you see that in the future that, that will. The CDO will be very separate to a kind of a CIO and therefore have a separate like. And it should have a direct link into a CEO. Probably.

Maybe you're a bit biased. I don't know.

Speaker A: 00:56:51

I don't know that it's, I don't know that it will report to CEO. I'm not sure. It depends how involved a CEO is within an organization which nowadays I suppose.

Speaker C: 00:57:03

It depends how tech savvy the CEO.

Speaker A: 00:57:05

I think it depends on the organization. Right. So I mean one COO in one company is different to a CEO in another company.

Speaker D: 00:57:10

Right?

Speaker A: 00:57:10

It depends how involved they are with the solution that you're putting together. If the CEO is completely business focused and generating business and they don't care about even the tech, so why would they care?

They don't really care about essential, you know, normal operations. They care about bringing in revenue into the business. It depends on the organization.

Certainly I would say, you know that the move from data office into tech is Data Office into like an opera of pure operational function. So the COO function or it moves into like a revenue function, like a CRO function, that kind of thing.

But certainly sitting on a, on a, you know, on, at the big table, I can put it that way. But whether reports as a CEO, I'm, I'm not, I'm not sure.

Yeah, I mean saying that, saying that's at my last place, you know, I had a dotted line into not my last place. So that's a lie. Ten years ago when I was, but when I was in Bermuda it was I direct.

I didn't report solidly to him, but I had a dotted line to the CEO.

Speaker C: 00:58:06

So I think, I mean also you've seen lots of COOs now are becoming from CI they're going through the CIO route because I, I guess you, the, the COO role will become a lot less focused about people and stuff like that.

As, as things like AI and stuff like that come into play, you're probably more thinking around how, how can we use that to, to limit the number of people we've got in big call centers and all that kind of stuff rather than kind of like that. So that that kind of, of old school operation aspect will, will probably diminish.

Speaker A: 00:58:37

Yeah.

Speaker C: 00:58:38

So it'd be interesting to see how that pans out. Right. We're coming towards the end now so I always ask a bit of a quick fire round.

First question is which brand or company do you most admire and why?

Speaker A: 00:58:50

I don't, I don't know. You know, you know I've thought about this before and that's supposed to be a quick fire.

Speaker C: 00:58:59

Does not have to be that quick. You can think about it.

Speaker A: 00:59:01

If I, if it was quick, fine. I had to give you an answer really quickly. It would probably brand Springboks. I'm a massive rugby fan. A massive passionate. So brand.

Actually I don't know why I'm thinking about that. Brand is Springboks then.

Speaker D: 00:59:11

Yeah. There you go.

Speaker C: 00:59:12

Cool.

Speaker D: 00:59:13

Yeah.

Speaker C: 00:59:13

Next one is. What's the one piece of advice you wish someone had given you when you were first starting out?

Speaker A: 00:59:20

If I had to say that. So my son goes into. Let's say my son goes into it. I'm going to give him advice as soon as he's finished school and he goes to uni and he studies.

Studies it. If he does, I will say to him go into first job. I would say go into a consultancy.

Speaker D: 00:59:35

Yeah, yeah.

Speaker A: 00:59:36

Go into a consultancy focused on insurance or, or you know, I love insurance anyway so I'd say insurance but bfsi, whatever. Go into somewhere or a small enough company where you can learn more. Don't go into this is my advice to, you know, my son.

Don't go somewhere where you're going to be a. When someone's going to give you a task and that's all you're going to be doing for the next three years. Go somewh where you can learn everything.

Yeah, right. And you get that, you get a lot of that with consulting, right?

Speaker D: 01:00:05

Yeah.

Speaker A: 01:00:05

So as much as you can learn in those first three years that aren't just if you're going to become a programmer programming, that's where you should go. And I think that's. You'll learn a lot in a consultancy or a small organization where you're involved with much more than just your day job.

Speaker D: 01:00:18

Yeah, yeah.

Speaker C: 01:00:19

That's interesting. I've always thought those kind of grad schemes that consultancies are quite.

Because I don't need to put you in like one thing for three months and you do something completely different. Different for three months.

Something like that and you move around so you can actually, you might have an idea of what you want to do but actually then you, like you said, you figure out you love doing something completely different.

Speaker A: 01:00:37

But yeah, well, case in point I'm taking, I'm taking a grad into. Into my team and that's exactly what I'm doing is that I'm moving him around my different teams so that he gets.

Because I want him to have this, you know, as much experience across the board so he can make a decision after six Months and tell me I, I want to work there and then I'll, you know, put him into that place.

Speaker D: 01:00:56

Yeah, yeah, yeah, yeah, yeah.

Speaker C: 01:00:57

Great.

Speaker D: 01:00:57

Yeah.

Speaker C: 01:00:57

Good. Really good advice that if you could swap jobs with one person for the day, who would it be?

Speaker A: 01:01:04

Maybe what's his name again? Is it Satya Nadella? The CEO Microsoft? All right.

Speaker C: 01:01:09

Okay.

Speaker A: 01:01:10

Yeah, I'd say him and purely because I just want to see how it works.

Speaker D: 01:01:15

Yeah, yeah.

Speaker A: 01:01:15

I just want to see how it's. What does a day look like? What is that like on his. You think about it, what is he doing every day?

How big is that company and all the things that are coming up with. Probably him.

Speaker D: 01:01:27

Yeah, yeah.

Speaker C: 01:01:28

It's funny you say that. I was listening to a podcast this morning with the. I forget his name now, but he's the CEO of Salesforce. And I mean, he, he's.

I mean, if you, if you, you would never think it if you listen to. He still seems like he's the CEO of a startup. In fact, he says that's how he sees himself.

But he rattled off the kind of numbers about like how kind of market cap and where it's like billions and, and I thought exactly the same, like, when you're CEO of a company that big, like, where do you spend your time? What do you mean?

Speaker A: 01:01:59

Yeah, what does the day look like?

Speaker C: 01:02:00

Yeah, yeah, it's, it's, it's huge, isn't it? And I'm, I'm listening to Elon Musk's like autobiography at the moment, and it's like, I mean, the great, the stuff the guy's done is, is like.

Yeah, same thing. How do you, how can you do all those things?

Speaker A: 01:02:15

That's it. Yeah. So it's not even. Not even. I mean, it doesn't have to be Nadella.

Speaker D: 01:02:20

Right.

Speaker A: 01:02:20

It could be any one of those big CEOs. What does the day look like? Like, I have no idea.

Speaker D: 01:02:24

Yeah, right.

Speaker A: 01:02:25

I can, I can comprehend my job, but what does that look like? I mean, that's, yeah, that's next level.

Speaker C: 01:02:30

So it's fascinating. Yeah, yeah, yeah, Good one. Best kind of business related book you've ever read or.

Speaker D: 01:02:37

Business.

Speaker A: 01:02:37

Right. Well, none. I'll say non fiction then. Okay. Non fiction would be Long Walk to Freedom. Mandela. Yeah, it's amazing book. Yeah, yeah, that's awesome book.

Speaker D: 01:02:47

Yeah.

Speaker C: 01:02:48

I mean, I'm on this. I said in the last one I did, I've made a kind of New Year's resolution. I don't really do them very often, but I'm going to Read a book a month.

I'm not a big reader at all. You mean, to be fair, with three weeks in and I'm kind of on track, so I'm taking that to win.

But I'm starting to build up the list of ones to read, so I'll add that to it. Best career decision you ever made, Warwick.

Speaker A: 01:03:10

That's got to be wife, kids. So without that, I wouldn't have met my wife. Wouldn't add kids. So it's got to be Warwick. Yeah. Best. Best move was transition.

Moving from London to taking the risk and moving from London to Warwick.

Speaker D: 01:03:23

Yeah.

Speaker C: 01:03:23

Amazing. And the one person that you kind of admire the most, role model.

Speaker A: 01:03:33

I'm just a cliche on.

Speaker D: 01:03:34

I.

Speaker A: 01:03:35

It's. I would say that. I would say. Well, I mean, it has to be. Every South African is passionate about, you know, Nelson Mandela, I would say.

But if I'm going to say a person right now, it's got to be Russia. Rasmus. So Rassi Rasmus is the coach of the Springboks, if you don't know. And, you know, he's taken them from down here to up here.

He's united the entire country. Everyone absolutely loves rugby now. Well, they always did, but it's. It's to the next level because of a coach.

I mean, they're giving him an honorary doctorate. I mean, he's got a PhD at the moment because of what he's done for. For the sport and the country.

Speaker D: 01:04:07

So.

Speaker C: 01:04:08

Yeah. What's the. You mean. I'm not a massive rugby guy, but I. I just. I follow England a bit and obviously you guys have given us a paste in once or twice.

Is there much behind that? Is it like.

Speaker A: 01:04:20

Oh, yeah, his whole. His whole coaching. So, I mean, if. If you don't know rugby, I mean, last season he played 50 individual players. There's a 15.

There's 15 people in a rugby team. He played 50 or 51. I can't remember. Whatever it was. Now, you think about any other international side, they'll have their set, whatever. 20.

They may play up to, what, 25 players in a year. He literally. He built up team, but like three teams of Springboks by swapping in players, new players, like bringing them in.

Speaker C: 01:04:49

He's got a formula that obviously is structured at work.

Speaker A: 01:04:52

Exactly, yeah. I mean, he's. And he's, you know, he's selected his. The captain. I mean, everyone thinks the captain, Sia Khaleesi, is an absolute hero within.

Within South Africa. It's. Yeah, it's taken it from more Than a rugby game. Yeah, to. It's almost like a cult following within. Within South Africa now.

Speaker D: 01:05:10

Yeah, it's.

Speaker A: 01:05:10

It's huge.

Speaker C: 01:05:11

So amazing. And then the final question I ask everyone is, what's the best thing about working in insurance? You already said you love it, so.

Speaker A: 01:05:20

I do love it. It's great. Christmas in the Square Mile.

Speaker C: 01:05:23

Yeah, that's a good one. Yeah, definitely.

Speaker A: 01:05:29

You know, in all seriousness, the thing about insurance that I have found, I mean, unless you're working for the really big ones, the Square Mile is full of insurers that are like whatever sub, you know, 500,000 people, you know, wherever it's going to be, you get to know everyone. The culture within insurance is unlike anywhere I've worked. It's vastly different to consulting, it's vastly different to banking.

It's just got a very. And I know it differs by company, but it's very collaborative. I mean, I still meet up with guys I've worked with.

Ten years ago, we met up for drinks at Christmas. Right. So it's. It's just the culture within insurance is. Is awesome.

Speaker C: 01:06:04

Yeah, it's very unique. That isn't. It is really the fact that you can just kind of like. It's quite good for me. I can base myself in one place and walk to all my.

Speaker A: 01:06:13

There you go.

Speaker C: 01:06:13

Ten minutes exactly.

Speaker A: 01:06:14

Yeah.

Speaker C: 01:06:14

Well, look, what a great place to finish.

Speaker A: 01:06:16

Thanks.

Speaker C: 01:06:16

Thanks so much for your time. It's really good. I could talk about the AI stuff forever, but look, I'm sure there'll be some people that want to reach out off the back of it.

So whether it be of talk more about kind of data or other people want to get your insight. So are you. Is LinkedIn the best place to kind of get.

Speaker A: 01:06:32

Connect? I shouldn't say probably, but I add everyone. If you add me, I add you. It's not. I don't reject really anyone.

Speaker D: 01:06:39

Yeah.

Speaker C: 01:06:40

Good.

Speaker D: 01:06:40

Yeah.

Speaker C: 01:06:41

David, thanks.

Speaker A: 01:06:42

Thanks very much.

Speaker B: 01:06:44

And that's it for today's episode of beyond the Desk.

Speaker C: 01:06:47

I really hope you enjoyed hearing from.

Speaker B: 01:06:49

Today'S guest and that you've taken away some valuable insights to fuel your own career journey. If you liked what you heard, don't forget to hit like and make sure you subscribe so you'll never miss an episode.

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