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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.

Whether you're just starting out or looking to level up your career in the insurance or insurtech world, this podcast is packed with valuable insights and inspiration.

So grab your headphones, get comfortable, and let's jump into beyond the Desk.

Chris, welcome to a podcast.

How you doing?

Yeah, really good.

Excited to be here.

Good stuff.

So I'm gonna go right back to the start of your career and we're gonna go through the journey.

Okay.

But first of all, do you want to just give a quick intro on you current role and then we'll go back and work our way through it?

Yeah.

So Chris Pierce, currently chief data officer at a personal lines insurance company called eShore, just been through a five year transformation project there.

I look after, well, a lot of areas actually.

Our enterprise data platform, so the infrastructure and architecture.

Underneath that I've got a team of data engineers, data data analysts, data scientists, AI engineers, and I look after data governance as well.

So it's quite a few hats.

Yeah, definitely.

And we're going to get really into that for sure.

So let's go right back to the start.

What did, what did kind of early childhood days look like?

Were you always into technology and that kind of thing?

Kind of as a kid, yeah.

That's a long way back.

Yeah, yeah.

To be honest.

And it's always a great thing.

Looking back with hindsight, I was like many kids these days days.

I was really into video games and computer games back in the early 80s when those things sort of really penetrated the UK market.

Yeah, I'm like big deals right to, to our generation back then.

Yeah.

You know, I was hooked from an early age, probably about five, and then never really let that interest go.

No, Obviously at age 5, it's it's all about, you know, being Super Mario.

But I think as I, as I got older, I started getting more interested in the hardware and the mechanics and the code behind how these things worked.

And I think that ultimately led me to having quite an interest in mathematics.

Yeah.

Which is ultimately the course I ended up taking through, through most of my education, not realizing that would be the direction of travel at the time.

And then those two interests just sort of further cemented and compounded over time to a point when, of course, things like data started to become quite prevalent.

It just seemed like quite a natural F to me.

It's interesting you said.

So did the, Was the interest in technology, computers, coding, etc, Is that what stemmed the interest in, in maths or, or was it.

Because it, Because I think typically it'd be the other way around if someone would be quite good at maths and then they'd get interested in software engineering.

Yeah, because it kind of was a.

I mean, made sense for me.

I, I didn't find, you know, maths Interesting.

Age 7, I defined computer games in just.

I was a big Mario fan, a massive Mario fan.

And Zelda as well.

Yeah.

But, you know, I, I think as I say, as I look back, I think what it was is that the context of a game is you sort of, you learn a rule set, you are presented with a challenge or puzzle, you play, you fail, you optimize your approach, maybe you create a strategy.

Over time, you end up winning.

Yeah.

Right.

And then you try another game.

So new rule, new system, new pattern.

And I think through doing that over and over and over again, you know, you, you develop almost a portfolio of skills about how to problem solve.

Yeah.

And how to make your mind versatile in terms of taking on new challenges and being able to learn new rules, new settings, new systems.

And that, that just stimulated me mentally.

And, you know, I, I found in similar ways the subject of mathematics when I was old enough to appreciate it also.

Scratch that itch, albeit in a slightly different way.

Yeah.

Yeah.

Okay.

So what did that.

So, so that's kind of school.

What did that evolve into?

Did you, did you go on to do university and stuff and was it still maths that you did?

Yeah, so fairly traditional in, in that respect, maths at a level that was sort of, I would say my favorite subject, but the one that seemed to come with the least amount of effort, just kind of a strategy.

Yeah.

Went on to do a MA degree at Manchester University and that, that fast followed with a Master's of Mathematical Sciences where it was Slightly more applied.

You know, I think my undergraduate taught me that the theory was, was less what I was interested in and really more of the real world application of that and solving real world problems was really what triggered me.

You know, following that almost through a bit of luck and serendipity really a couple of years after I finished my masters, ended up going back to university study for a PhD in a particular branch of mathematics around epidemiology and probability.

That was never the plan, as I say, various circumstances that look to me doing that and it certainly wasn't something I ever imagined I could do.

Ironically, that probably opened up the door to the career and the position I'm in today.

What made you go back and do the PhD?

So did you, do you say you did a couple of years work?

Yeah, so after my Masters I did a small stint in accountancy and just found that that wasn't really compatible with what I was really, really looking for at the time.

Yeah, and then I worked in a bank in a number of different capacities.

I started out as a risk anal, so that's what it says on the Tim.

Right.

Assessing risk across the business and across their investments and trying to manage the portfolio spend there and also as what was called a credit scorecard on list, so building statistical models ultimately to try and decipher who should and shouldn't get, in that case, unsecured lending products, so personal loans mostly.

So that was sort of the start of leveraging data and statistical systems in a commercial setting, albeit a heavily regulated one.

You know, I think a couple of things led to my return to education.

One being the financial crash sort of in the late noughties, where of course lots of jobs in many industries were suddenly at risk and certain areas of companies were, you know, having mass layoffs and having to shut down quite, quite quickly.

You know, that created a lot of uncertainty and voluntarity in the company that I was in.

And almost again by sort of chance, one of my old supervisors had sort of come into some funding for particular research and just sort of tapped me up and said, hey, I remember you are quite interested in this.

We've got some investment.

Do you want to come along and consider some of the courses that we're considering funding?

So the two things just sort of conspired to me to think, well yeah, I'll go and explore those options.

And ultimately that led to something that I thought would be interesting.

You know, it was paid for at the time, not much, but you know, it was an opportunity to test, you know, can you do this.

Are you up for this challenge?

Is this something that you can really sort of throw yourself into and contribute something new and meaningful to the academic field?

And how does that work?

And I mean, I've got very limited knowledge on how PhDs work, but they're ultimately research based.

Yeah, fully.

It's funny because I've had quite a few people in the past 20 years talk to me about my experiences because they're thinking themselves, is this something for me?

And as I said, I never thought that would be something for me personally.

I always like to describe it as the reverse to education up until that point.

And by that I mean education up until that point is often, here's a curriculum, here's a prescribed set of information.

Yeah.

We want you to learn it.

I know it's not quite the same anymore, but back in my day it was certainly no, you're going to sit in an exam room and regurgitate all of that.

And the more you can regurgitate, the better result you get.

Right.

And so you started education basically not knowing anything and came out of education thinking, I actually know quite a lot about that subject.

Now a PhD is almost the exact opposite.

You go into it thinking, oh, I know quite a lot about my subject.

Yeah.

And you come out of it realizing, my goodness, I know very, very little.

There's been so many clever people over the course of history that understand this and have contributed this area in all sorts of ways I couldn't or I conceive of.

And so, you know, you're thrown into this sort of overwhelming world where you've got to try and A, understand everything that's come before, and B, navigate your own path into finding something novel.

And then, you know, use every tool in your toolbox to try and create a compelling set of research that has a, for one of a better phrase, a useful outcome.

Yeah.

There's a bit more real world in that sense.

You're not, you're not learning just what, what you mean typically in a, in a degree or something like that, you're learning what's happened before.

And that's as a precursor to what you might do when you go out into the real world.

Yeah.

And you're, you're, I mean, technically speaking, you're not on your own, but often you feel on your own, you know.

Yes.

You have the support of your supervisors and your peers in your research group.

Yeah.

But you are the one whose responsibility is to go out and find that something new and figure out how, prove and demonstrate that with, with Very little prescriptive guidance.

Right.

Yeah.

You'll get suggestions and steers, but ultimately it's all on you.

That responsibility and the ownership, I would say is.

Is at a different level to education proceeding.

And that ultimately makes the game, in my opinion, much more about, you know, resilience, perseverance, you know, you will try things, dozens of things, and fail.

And you just have to pick yourself up and change angle and, you know, and it's almost a battle in your own mind about how you can persevere through that and motivate yourself over the long term to keep at it and keep going and have confidence in your own abilities that you will find that way.

And how long does that.

So how long do you do that for?

Mine was fairly standard.

It was over four years.

You spend the first three years effectively figuring out your contribution and then you get sort of the final year to write all of that up in a thesis.

So that's three years of research.

Research, is it?

And researching what you're doing.

Testing things.

Yeah, Lots and lots of reading, lots of research, lots of speaking to all sorts of people nationally, internationally.

Yeah.

And then just trying things out.

Yeah.

What did you do your PhD on?

Did you say?

Epidemiology?

It was epidemiology, yeah.

So which you probably weren't.

Weren't.

I know, but that became quite.

It was a BBC every day.

Yeah, yeah, exactly, yeah.

What was that guy that was on there like the, the.

The guy who's on the news all the time?

Yeah.

His name though, ingrained.

Chris.

Yes, it was Chris, yes.

Which you think you'd be able to remember.

So.

Yeah, it was epidemiology.

It was.

It was stochastic probability.

So it was.

It was basically looking at.

Similar to Covid actually diseases like the flu and the cold.

So by their nature you, you become infected, you go around for a period of time infecting other people, you recover, but then later on you can become resuscitible to that disease again.

So it was looking at sort of population dynamics, how quickly diseases of that nature would become pandemics.

And given that when the right sort of intervention points might be for EEG vaccination program.

So lots of sort of statistical simulation and probability around forecasting that type of behavior.

So several years later you had a kind of unique insight into what was potentially going on in the way in the world, which is.

Which you obviously were never to know.

But.

Yeah, yeah.

So.

So how did that.

So what did that look like post.

Post PhD then?

So you did that for four years I imagine that was quite intense.

Yeah, I think, you know, for me after those four years that was sort of enough in my mind to say, you know, no more academia for me.

You know, I, there was the option to make a longer term career out of that, but I, I found that the subject, you know, yes, it was applied in a very specific area but, but there was very little wiggle room beyond that unless you went and learned a whole different branch of mathematics.

Also pitching for funding was.

It's an interesting environment, just one that wasn't really for me.

So I, you know, I, I started looking for roles in data.

You know, data was starting to become part of the narrative in many companies.

You know, being data driven was the cliche at the time.

What year this had been.

This was 2011.

Okay, 14, 15.

Yeah, that the term data scientist had just started to sort of come out in, in the us.

It wasn't really in the UK market at the, the time, but you know, websites were starting to talk about that.

You know, it was branded at its infancy.

You know, sexiest job of the 21st century.

You're getting all that sort of narrative coming out of the west coast.

I went to an FMCG consultancy in London and ultimately their job was to analyze Tilraw data from the big retail supermarkets.

And those markets would ask them questions, you know, largely around discounting strategies.

You know, what sorts of products do I put on, what types of promotions when so I can maximize revenue or.

Is that aligned to like kind of a nectar card?

Exactly.

And yeah, so strategies for how they could build and develop their loyalty programs as well.

The Tesco's were much ahead at the time.

Still, many supermarkets back then would use consultancies for advice and steer and strategy on how to do that.

Even really interesting stuff like how do I arrange products on the shelves, drive direction around my supermarket and get customers to stay in there for longer.

Yeah.

So, you know, I would do lots of sort of bespoke services, you know, again, much of which would involve statistical modeling, you know, facing off to those clients and, you know, explaining that the work that I'd done and trying to convince them of, you know, what the right thing to do was.

Sometimes they'd listen, sometimes they wouldn't.

But that's sort of where I cut my teeth.

And that was sort of really my introduction to another term that became popular a few years on big data, you know, and sort of the implementation of mathematics through programming and how that could be used to genuinely boost revenue.

I just found that really fascinating.

So how Did.

So what was your role then?

Was that a kind of a.

Like a data strategy consultant?

I was, I was a statistical modeler in a branch of the division that was referred to as expert services.

So it was sort of like bespoke projects that had some sort of forecasting or predictive elements around them.

And I would be part of a team that will be responsible for sort of curating all of the data that we would then use to model off, sort of extrapolating the answers to questions, scenario testing and then coming up ultimately with you know, a compelling answer and proposition off the back of it.

Yeah.

And so how did that evolve from there into kind of.

Because obviously when did that evolve into kind of leadership?

So I guess at this point you're mainly client phase.

Yeah.

Doing the work on the ground.

But it's not an IT technical data.

Role, it's more very much I see, you know, lots of sort of what I would describe as back end R and D work with a little bit of front facing and all the pressures that come with pitching to clients from there.

I think like many data scientists in their early career, at the time my mindset was on sort of.

I really want to sprung the breadth of the sort of algorithms that I'm getting exposed to and the different kinds of ways in which these can be used to solve problems outside of the context of supermarkets.

So from there I moved on to Mercedes Benz up in Milton Keynes at a time when they were launching their A class model, which, which back then was like their first frame to the compact car segment.

Yeah.

So there was huge investment, particularly in marketing around this model, as I say, entering a brand new area that at the time was dominated by Volkswagen in particular.

And there was a lot of uncertainty about how they would really penetrate that segment.

So big investment in marketing, you know, above, below the line, lots on digital and big questions around, you know, how do we package up this proposition?

What sorts of offers can we, can we give to people?

How do we tailor it to individual customer segments?

You know, how do we position our website, you know, banner advertisement, PPC bidding.

How can we push this product to the right people at the right time in those journeys?

Yeah, you know, how, how do we market this in terms of all the ancillary options that are available, which actually surprisingly complex when you, when you talk about prestige cars.

So lots of what I would call sort of CRM, website design, traffic flow optimization, customer segmentation, that sort of stuff then helped by the fact I'm a bit of a petrol head as well.

So that was a big pull.

And yeah, again we were part of a small, what I would call an insight department.

And at the time there was no insight on this model.

Right.

So it was sort of come in and build out that analytics and that education piece and that strategy around that product for the business before they took quite a big gamble on it.

So, you know, again, that sort of got me really exposed to, you know, being in a sort of bespoke hub that was center and foremost to a strategic direction for a new product.

And again, lots of ownership around that still very much.

I see.

Yeah.

A couple of years later, the opportunity then came to get a little bit more exposed to leadership, I guess.

So I took a role with Money Supermarket, who at the time new office, central London, mass recruitment drive to fill that space with huge array of technical challenge.

It's really interesting.

It was sort of everyone in there on day one together, which really helped with the camaraderie.

Yeah, yeah, still marketing, but this time the agenda was more we want to re platform and we want to build out sort of an engine for campaign optimization.

So ultimately how those businesses make money is through people coming back to their website and switching their provider in whatever insurance vertical they have.

That's their revenue model.

So ultimately most of the investment is into marketing to make sure that customers come back to their website and switch.

Right.

So I supported building out the platform, building out machine learning, proprietary machine learning capabilities that would sort of analyze all of the clickstream data that what people were doing on that website, what they were quoting for, what they were clicking on, what they were viewing, what options they were playing around with.

You know, these companies have effectively full visibility of the entire UK adult population, right across all of their insurance holdings.

So the, the amount of data they have is incredible.

Yeah.

And were you there right at the start?

I was there right at the start of that sort of bespoke department, if you like.

And through that experience I started to get involved in building out a small team to begin with and started getting involved in slightly more broader and peripheral tasks beyond data science.

So again, digital architecture, sort of what is now called machine learning operations.

How do you actually productionize code to impact the real world and how do you make sure that can react in real time in somewhere that's useful?

And how can you, how can you manage the speed and the cadence of that sort of cycle, if you will?

So that it was that gig that was sort of the beginning of getting into, into leadership.

What year would this be now?

I was trying to think no it would have been about 2014.

Okay, so kind of 10 years or so ago.

Yeah, yeah.

I had a great couple of years there.

Again, sort of.

It was almost like a startup mentality.

We had a huge job of us.

It wasn't necessarily that well defined in the how we knew what we wanted to do, we knew what the vision was, but there felt, at least at the level I was there, a lot of autonomy to go and shape how we wanted to execute on that vision and drive that agenda and with a really strong team around me as well.

So that was really great.

And that was sort of a really empowering experience for me in terms of, you know, coaching, mentorship people, leadership and just sort of galvanizing lots of different teams that required to execute on, on what we were there to do.

And then, and then what.

So, and so when did that evolve into.

Was because that would have been not necessarily directly insurance, but, but kind of some, some products would have been aligned to it.

Yeah.

Is that, is that you kind of did it carry on in insurance after that?

It did.

So I, I still like to, I mean, technically those companies are marketing companies, as I say, but I, I still like to think of them because the synergies and the commercial models between the aggregators and the insurance companies themselves are, you know, that they go hand in hand, though it hasn't, hasn't always been as frictionless as that.

But they've evolved to be really dependent on each other.

And the data products that they share between themselves as well is, it's also, there's a lot of symbiosis there.

I was, I was approached by an insurance company and sort of.

I'd had a lot of exposure of trying to sell data products to insurers and hearing of some of the challenges that insurers had, particularly around knowing your customer, which, you know, the aggregators have huge wealth of information on.

And I was just intrigued.

I thought, well, actually maybe seeing things from the other side of the fence or at the time I thought actually I might be able to bring the experience that I've had and seen into that industry to help it in a slightly different way, maybe think slightly differently.

Whether that was true or not, who knows?

But I, yeah, I got an opportunity with insurance company Hastings Direct, who had been acquired by a private equity company at the time, which was the case in many businesses at that sort of life stage.

Big investment, very clear idea on what worked and what didn't, you know, come in, help embed and scale machine learning, sophistication, build out A team scale that find ways to add value.

Value galvanized the business around adoption and use of data science.

And, you know, it was a bigger opportunity.

So being honest at the time, insurance was something I'd never really thought of myself gravitating towards.

Yeah.

I still think there's quite a bit of stigma around the industry in General.

You know, 10 years in, I would say it's completely unfounded.

It's one of the most exciting industries there is.

Yeah.

But, you know, I took that chance.

I went in and, you know, 12 years later, I've.

I've not looked back.

So that was sort of the start of my insurance journey.

What did you, what was the first role at Hasting?

Did you go in into.

In straight into a leadership role?

No.

Well, yes, I was a.

I was a senior data scientist.

I was responsible for building out a.

Initially a small team of data scientists.

And I was focused specifically in their retail pricing division, which is sort of the key lever of profitability for insurance companies.

So lots of opportunity to rethink how pricing was facilitated, the data used around it.

Lots of opportunity for machine learning, extremely complicated setups.

Many insurers have, you know, predicting both risk and consumer behavior as well and how you, how you build all of that into sort of an optimization framework.

It's really fascinating.

And of course, ultimately there's no fixed cost to insurance.

Right.

It's not like a Mars bar where you work out logistics and distribution and other margin.

You're kind of making it up.

So that adds a whole different dynamic to how you manage that.

And yeah, so I spent three years in that area and ultimately ended up as head of data science in that space and got the remit to build a bigger and bigger team.

And then over the course of the next few years there, I had the opportunity to move into different parts of the business and sort of grow out other smaller teams in different areas.

And then ultimately at the end, the chance to sort of consolidate and centralize all of that into.

Into one function.

Yeah.

So did you leave when you left Abe?

Were you in the CDO role at that point?

No, far from it.

Still very much data scientists by bread and butter.

But I'd been, by that point, there'd been three, four teams that I'd sort of built out from scratch.

All data science seems just in different areas of the business.

Yeah, okay.

But I'd ended up with sort of exposure to the breadth of the insurance business model.

So, you know, fraud, claims, telematics, pricing, marketing, premium finance, reserving all sorts of different businesses in their own right in some way.

So sort of every facet of data science was applicable across all of those different domains.

So, you know, really quite lucky to have ended up in a position like that.

It's not common still to this day.

And then, and then Esau came along again, similar opportunity if I'm honest.

You know, PE backed, you know, come in, build out a team from scratch.

Again, centralized scale, you know, very, very exciting vision.

At the time they, they were very particular about wanting to differentiate themselves and their, their seriousness on data I felt was unparalleled at the time.

You know, I felt I'd certainly learned a few things about what not to do from, from my previous experience and you know, my eyes were sort of wide open in terms of what I thought I was coming into or at least I thought that at the time.

Yeah.

And so I, you know, I entered there again as sort of head of Data science and you know, held that title for a number of years before ultimately becoming the the Chief Data Officer.

Y.

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Now let's get back to today's episode.

So we'll see that's there's quite a shift change there in regard throughout the even the time that you spent in the 10 years in insurance from from being a kind of hands on data science.

Well actually going through the journey of being a data scientist before data science really existed in, in its, in the way it is now but then almost kind of seeing that evolution of of a role type and a kind of a business unit evolved, being instrumental in growing those units and, and then evolving into the chief data officer role, what would you say the kind of the challenges you faced in moving into kind of leadership roles from being the guy who does the data stuff in really basic terms?

Yeah, it's a really good question.

There's lots of challenges, I think.

I think because I sort of entered the workforce at the sort of beginning of the data science wave, so to speak.

It's always been to some extent the case that you're always going into a business to try and develop something new and different, or you're trying to work with people to adopt perhaps a slightly different way of operating in order to exploit the services or the tools you're building for them.

And that's actually really difficult to achieve in practice.

And even if you solve that sort of influencing and adoption challenge for them not to go on and evolve into something that is routinely driving value in a sustained way and owned by ultimately those end users, it is something that just.

It's not once and done, it's continual and always evolving.

And so I think it came to a point where, you know, I'd had a lot of technical exposure.

I built up a repertoire of knowledge that was relatively versatile at the time.

You know, I'd had to reskill many times over in different languages as they fell in and out of popularity.

But ultimately, I think it was a realization that, you know, it was so much more than just, you know, clever coding and some numbers on a piece of paper that meant your model validated.

Well, the interesting challenge for me started to become about, okay, I've got to really try and sell this to people, and everybody's different, and everybody has different agendas and different concerns and different emotional reactions to something new.

So.

So the challenge of overcoming those to actually get adoption in business really became the thing that I started to gravitate towards more.

And that inevitably led to just becoming really interested in the business people's jobs and just proactively going out and tell me about what you do, tell me about how it works, tell me about your problems, figuring out how I might be able to help in that, and then working with them together on figuring out what that would look like and how it could be achieved.

And everything suddenly then became less about, you know, I'm this boffin in a box doing all this clever stuff on a keyboard to actually partnering with other people to solve their problems in a way that could leverage those skills.

And I think for me that was the sort of, that was the twist in the story that got me really interested in sort of the broader challenge of how can you mobilise a business to take advantage of this capability.

Not least, I'd had my own experiences and had heard in many other people's, you know, people I'd interviewed or peers of mine, experiences about how companies would hire data scientists and then straight away they wouldn't be, they wouldn't have the tools they need to do the job or they couldn't drive value and six months later they were looking for another job.

Right.

And that was really commonplace at the time because companies or hiring managers just really didn't understand what it was or what to do with it to really drive tangible outcomes.

And I felt that that's really where I can try and make a difference.

Yeah, I think that that transition from, from kind of technologist or, or kind of someone who's in the detail to someone who's leading people and leading teams and strategy and stuff that normally comes with the, the understanding or the realization that the, the business problem and the partnering that you, you just mentioned is, is, is like the, the core of it.

There's lots of people that don't ever evolve from that.

Right?

Yes, because they're just, they're really just head down in the, in the detail stuff and that's what they love doing.

What, what did, do you think that for you?

That, that, that kind of vision of, of moving up the, the kind of ladder, as it were, into a CDO role, leadership role.

Was that something early on when you started that you saw, did you always see yourself as being one of those types of people having a, that point, or did you just continue doing interest?

I mean, no is the short answer.

Never in a million years did I envision, you know, my career as, as it has elapsed or transpired.

You know, in my early years I was very much an IC that was really passionate about my subject matter and just wanted to be the technical expert and know as much as I can.

And part of that was, was fueled by the PhD experience and also that, you know, I've been lucky enough to work in a field that's full of ludicrously clever and capable people.

Yeah.

And you kind of want to learn from them and keep up with them and, you know, help, help them as well, which comes with its own pressures.

But I think, I think when I, what I recognized after a few years, I mean, I was always ambitious and I was always keen to try and test myself and push Myself to see what I was capable of.

You know, that's just always been part of me.

Call that only child syndrome, I don't know.

But it was really when, you know, it's the challenge of bringing people together and influencing people successfully combined with the actual reward of seeing something go into the real world in a way where you can see the value and those people that you've worked with can also see that same value.

And then it's evidenced through data.

So it's, it's ubiquitous to everyone.

Yeah, it was that cycle that really drove sort of satisfaction in my work.

And so I think I, from that point on I was, I was sort of addicted to that and I wanted more of that.

And inevitably that meant sort of raising myself out of the more day to day technical and going more towards the strategic side.

Though it's been a real challenge and still to this day, you know, I still want to be down in the technical and I still want to be as good as the technical people that, that I lead and work with.

But, but of course the, the shape and the remit of, of the requirements of the job change as, as you go into those spheres.

Yeah.

You know, data scientists can be difficult.

You know, you get their respect if you can relate to them on a technical front and it's hard to keep up.

But you know, I try my best to do that.

But at the same time, you know, being strategic, having that high level oversight, operating in a world of the exec and the board come with completely different challenges and pressures and skills and all of that is needed to create the chance for a data science team to be there in the first place.

Yeah.

And yeah, I think I'm in my happy medium now.

I was going to say like obviously you've been, you've not been in the Chief Data Officer role for too long.

It's your first role in it.

So you've obviously, but in some ways you've had the good experience of being able to do that in a business where you had already some kind of skin in the game, hopefully some kind of track record and good.

And therefore there's an element of goodwill in the sense that people will allow you to learn in the role.

What would you say the kind of biggest challenges you faced in flipping from.

I know there was a kind of some leadership in it, but I guess the head of data science role was still involved in doing data science, doing it sure to a decent, decent degree.

Whereas I suspect that's probably not part of your day to day right now.

I Think in the more strategic roles, certainly where you've got responsibility for budgets, for example, you're often competing for priority.

You're often competing for resources.

You often have to make peace with.

You're not going to get your way all the time or you're not going to get what you want when you want.

That's life inside and outside of work.

Right.

But because of those challenges, you have to take a bigger picture mindset.

You have to be cognizant and conscious of so much more than just your area.

Yeah.

You have to really hone helping explain the benefits of how your area can add value and why people should pay heedance to your counsel or your steer or your advice in really sort of crisp, simple ways.

It's psychology and it's people challenges.

Right.

But it was that, it was that, that blend of, that blend of sort of people challenges and that influential experience that, that I think was the differentiator for me.

Yeah, I don't know if that answers your question.

No, no, no.

Yeah, definitely.

And I mean it's, it's interesting.

I've had more other chief data officers on the, on the podcast.

Most of them have been doing the role for quite a while.

And I think what's interesting with your role is you're kind of ridden the crest of the wave with regards to the data science, evolution and kind of the start of that.

And now I get the impression we're now in a, certainly in my line of what we do, loads of data stuff and the data science team, as it were, has kind of cemented itself in insurance.

It kind of feels like it's a pretty core key bull.

Most businesses have got it, but still.

Hard to actually demonstrate value from despite that.

Okay, I think what is it?

I guess I think probably to answer your question a little bit better, I think you have to be pragmatic and often that means going back to your earlier point around building credibility.

Often that means you have to be tactical as well.

So it's about identifying the low hanging fruit, building prototypes fast that may be far from perfect, getting them in when the timing is right and the opportunity is there, demonstrating the value and then going again quickly and then building on that.

So again you start to get this reputation of can disseminate and dissect the problem, can target a solution and can deliver quickly and then iterate on it.

That, that for me was a difference in approach versus the more purist sort of I want to experiment with all these really complex algorithms and build the perfect solution and nothing will happen.

Until I've done that three months later, by which time the problem's gone and changed.

So I think that that was a really important component in the switch of requirements to sort of transcend that gap.

And that's not always easy to do, especially in the world.

You know, certainly with AI now where things are changing all the time, every week there's a new sexy thing that comes out and you sort of, you know, the purist in you wants to go, what's that?

Let's go and experiment with it.

Let's go and understand the value.

But also you've got a, you've got a business to run and you've got real problems on your plate now to solve which might preclude the ability to do that for another few months.

So you have to be, you know, you have to say no to the business, to yourself, to your team and then you've got to manage the consequences of that as well.

That's what that was going to be almost my next question actually.

But I mean we like there's.

So with that, like I say, that crest of a wave that you read, you have ridden with the data science piece that's now evolved into Chief Data Officer role which is obviously a lot broader.

But there's now the new wave of AI stuff which I imagine is front and center for all chief Data officers.

And, and, and like we were talking just before we started about like my new business is, is we're investing a lot in that kind of stuff.

And, and look, I'm not a technical person but I'm the person in our business looks after that kind of stuff.

And, and the hardest thing for me, and that is, is exactly what you just said, that there's every day nearly there, there is a new evolution of, of, of what stuff can do.

And unless you're moving super fast.

I'm testing stuff.

Stuff.

Yeah.

Then if you're, if you're going to spend two or three weeks even testing something, trying to build, I don't know, some kind of AI agent that does xyz by the time you've got it, got it built, there's a better way of doing it and you can do it a lot more quickly and that can, that can encourage you to just kind of stand back and watch.

But if you stand back and watch for too long, then you get overtaken.

So yeah, so I mean look, that's, that's in a, in a five person executive search business, you're operating in a, a much bigger kind of corporate environment.

So how do you deal.

How did, how's a chief data officer deal with that level of speed and change?

Because I know technology's always moved quickly.

It's now moving at a rate that's just in the last six months.

That's just unprecedented.

Right, agreed.

It's a good question and a broad one.

I mean, in some ways everything's different, but everything's the same.

And again, if I can, if I can just go back in history a little bit for context, you know, even in my time, I've, I've been through the wave of, you know, data science, big data, machine learning, cloud, you know, deep learning, reinforcement learning, AI.

Right.

In all of these cases there's been massive amounts of high hype and you know, by and large that's driven ultimately in the long term, good outcomes and genuine advancement in skills and capability.

Sure.

But it's also come with a lot of noise and confusion and miss selling in many ways.

So I think it's helped being in this industry to have gone through these sorts of hype cycles a few times now to understand that actually, you know, in the detail, it does take longer than perceived for a lot of these capabilities to mature.

And also there's a big difference between everyone's playing with it and people are using it in, you know, production type ways in the real world to drive actual value that is, you know, seen and believed and proven.

It's that it's the step from proof of concept through to revenue driver that is often the thing that takes a long time to do in practice.

So in some ways, you know, it's staying calm in the face of, you know, the acceleration of options.

And it is again, to sound a bit like a broken record, it is about really understanding your business problem and understanding what tools or algorithms or technology can be leveraged to solve that.

And you know, I think if you're good at matching that they can sort of focus your attention and your drive around the right thing for the right job.

The challenge beyond that is thinking ahead.

It's thinking, well, how can I make sure I've got the bandwidth to actually think about how I could completely reinvent the way we do something now that is a little bit more hedge your bets.

And you know, I found in those endeavors the best thing to do is to leverage your partner network and indeed your peer network in other businesses.

Right.

Get out their network, understand what other companies are doing, what challenges they have, what problems they're trying to solve, work with your business partners on proof of concepts or collaborations together, be transparent in what you're trying to do, work out an arrangement that's mutually beneficial for you to work on and solve that problem where resource might be strained or capacity might be limited or talents might not be readily available.

Yeah, yeah.

You mean.

I think that's.

Yeah.

You mean kind of benchmarking ideas and stuff like that is suddenly become a lot more important, isn't it?

I mean it's.

How do you, how do you see the.

I mean this might be a difficult.

Maybe as is easier actually.

You've only been the role not to not a few years.

But how would you see the role of a chief data officer evolving in.

In insurance specifically?

Because comparatively speaking it's a relatively new role for the insurance sector.

I mean I can still think of insurance businesses now that don't have a chief data officer or heads of data or whatever.

So it's still in comparison to the kind of atypical board low.

And actually the other thing is as well, it's often not a true C suite role.

It often reports into a COO or CIO or whatever it might be.

So there's different flavors of how it works, what it looks after.

It hasn't really cemented itself as the kind of standard role that looks after xyz.

So how would you see that kind of evolving over the next few years?

I would argue it's not just the insurance industry as well.

I think that the role in general is still relatively nascent and it's funny that the letters themselves can carry such different remits depending on organization and industry.

Some can be very sort of governance and compliance heavy, some can be very sort of machine learning heavy.

It's very non standardized, isn't it?

It's interesting, there's a famous book by Carutherson Jackson that actually talks about the concept of the first generation and second generation cdo which really resonated with me and it sounds obvious when you read it, but obviously the first generation CDO comes in and there's no data strategy and data strategies are extensive pieces of work because you've got to align your whole data ecosystem to every single business goal in a way that brings it all together and creates value for money.

So first generations sort of have this really undefined remit ahead of them and this massive, massive task to galvanize an entire organization and you know, improve data literacy for want of a better phrase.

Whereas second generation CDOs might be coming in to an already well defined and established data strategy and more sort of just cranking the handle and optimizing and refining that as we go.

Yeah, my role was very much a first gen and I think to your point, many, many companies are still looking for a first gen end.

The advent of AI makes it interesting.

I've seen AI sit under ctos, I've seen that, I've seen narrative that suggests that might be more towards a CTO role because a lot of it is software development.

Obviously a few places now are seeing the rise of the Chief AI officer.

Now that'll be interesting to see how you carve out a remit for that.

If there's a CTO and a cto, how do you really partition?

Others are just putting AI under the cdo.

So again I think it's going to take a long time to start to see ownership around AI and data and technology standardize if it ever does.

I think that is the first challenge for CDOs in insurance is establishing the ownership model.

Quite often it's normal to say anything that has the word data in.

It's the Chief Data Officer's problem to solve that.

And you know, reality is it's extremely nuanced.

There's all sorts of different data ownership models that can be adopted and implemented around businesses and you know, your job is to really sort of set that out and help the business understand how it needs to mobilize and how it needs to think about ownership of data across the end to end.

And then your job is to effectively facilitate giving the business what it needs in, in as timely a manner as possible and as safe manner as possible.

So now I'm not sure that answers your question.

No, definitely.

I mean it's an interesting talk.

I think the, the, the I agree with you in the, in the sense the, the, the evolution of the role is also self fulfilling in the sense that because it's, it's quite different in lots of different businesses as a result of that the people that actually do it are very different.

You, like you, you pointed out you could have chief Data officers that are very heavy on the governance and standards and therefore because they've gone in probably to a business that have very little governance around it and they've done a lot of work around that.

Equally the very other end of the spectrum you can have someone who comes from a kind of data engineering background and therefore is maybe more aligned to a CTO or something like that because they're ingrained in technology and then everything in between.

Whereas your typical CTO is generally follows the same the pattern software engineer into architect into kind of CTO or something.

Not everyone, but a distinct majority would.

Whereas in the, in the data officer world it's, it's, it's, it really is a complete kind of patchwork quilt of different types and as a result that the role and evolves being completely different in some, in some businesses, etc.

And you know, it's part of that is an education piece.

Now don't say that in a way that's intended to be disparaging to anyone but it's such a complicated area.

There are so many component parts to being able to store, dispense, utilize data in a well governed, well managed, cost efficient way and then how the business operates around that and what the right operating model is for it.

A lot of that is time spent on helping execs, you know, understand how that should work or at least proposing ideas around how that should work.

And often it comes back to sort of what I talked about at the beginning of my career.

Often that means rubbing up against the status quo and often that means sometimes friction that you've got to sort of work through and again you've got to sort of really sell the benefit often in very different ways to different people.

What do you think in your opinion, the kind of how that role should sit and how is it still very dependent on the business?

Yes.

Rather than kind of some utopia vision of what a seed CDO looks like?

Yeah, I think so.

As much as I'm a fan of standardization, it depends on size, scale, business model, commercials of the business products you do and don't sell the type of data that you do and don't have the third parties that you work with and the data that you source externally.

Some startups have fantastic, very forward thinking data models and operating processes but they sell one product so it's very sort of focused around iterating on one thing all the time.

You know, the context of that is it can be very complex but it's also very lean, big, you know, multi region multinational corporates with tens of thousands of staff operating in different cloud environments in different silos all over the place.

Considerably different type of challenge, you know, much broader in scope potentially.

So I think you have to, you have to fit the role to the business.

Yeah, yeah.

And we touched a bit on the AI stuff.

So, so what, what, I mean this is a, this is a big question but, but for you, what's the, what is the next couple of steps?

I'm not going to say years because that, that almost seems like, yeah, it's impossible.

Right, But I think, I think you used to start talking about the next kind of one or two years.

Now it's now it's probably kind of six 12 months and that might even be next two or three months.

But, but for you as a Chief Data officer, what are you looking at in regards to kind of the next steps around the evolution of AI and how you turn that into something that's tangible for a business that you work?

Yeah, I mean to be honest, the past couple of years for us we've really doubled down on generative AI.

We've actually got 16 use cases in production and many of Those use cases, 8, 900 frontline agents being assisted by in real time using that in some way to manage the conversation with the customer in the here and now.

And is it mainly customer service related stuff?

Yeah, customer service and sort of claims complaints management.

Very different paradigm doing that to eg, rolling out copilot to your staff.

Very different.

So you know, we've got a lot of experience in that and you know, we've learned from a lot of challenges in trying to do that.

I, you know, I think not many companies, despite the noise, are really deploying LLMs in production at scale yet even in just sort of single point use cases.

You know, summarize this, generate that because it is, it is challenging and it's expensive if you get it wrong.

Yeah.

Where I see the market evolving and, and forgive me for a couple of buzzwords here, I definitely think agentic AI will begin to become more commonplace in production.

I just don't mean in narrative in production.

So where you've got these sort of single point applications, how you can almost chain them together into some sort of system that is either part automating tasks away from people or in some way the sum of its parts can lead to some sort of decision or output that it doesn't need some sort of human intervention.

Now again, those things are very complex because often you're integrating lots of different software together.

But I think that will become much more commonplace over the next few years.

Will have ramifications for, you know, how customers interact with businesses and their expectations from them.

I definitely think, you know, at the moment AI, nearly all of us interact with it via text and bashing keys on a keyboard.

We will inevitably see that interaction start to happen through voice, which will be a huge game changer.

Again you think about the barrier to entry and how that will be lifted away completely for people being able to experiment with these types of capability, how you would manage and govern that is a big, big challenge.

But I think that will start to become more commonplace, especially in our personal lives and our mobile phones for example.

Yeah, and I, I also think certainly the big tech companies will start to package a lot of this capability up themselves and sell it as a managed service.

Because again, for, for companies that either struggle on having the bandwidth or getting the talent in house or have the capital to invest in these programs, it will just be easier for them to buy it off the shelf from a tech company that can provide that to them.

And again, I think it will only be at that point when we see sort of a step change in gen AI being more commonplace in practice.

Do you think?

Because I can totally resonate with that.

I told someone about it the other day, actually about the fact that there's definitely a skill shortage in AI related types of people, especially in the engineering space.

Data is even in kind of traditional data or semi traditional data type stuff.

It's still really hard to get good people.

I actually think that what insurance businesses will do will focus most of their efforts on the senior end of the market and getting in people who can lead strategy and delivery and stuff like that rather than the actual techies because they'll go down that route.

The only thing I can see where that maybe creates a bit of an issue then is you end up with this kind of vanilla way of doing it in the sense of like, I mean everybody's got, we were talking a bit, a bit off air about kind of esau transformation and, and, and, and everyone having similar claim systems and therefore the amount of which there's comfort in the fact that someone else down the road has got it.

But there's also then limited amount of competitive advantage you can gain from that because everyone's got the same thing.

So do you see that?

Do you see them just kind of providing the base knowledge and then you being able to build on top of it.

Is that kind of how it does?

Yeah, I'm thinking this in a non, my, my non technical brand.

I was, and of course I'm speculating here, but I would expect that they would package up sort of generic services that can be used to solve eg, you can implement this chatbot for your business or eg you can use this application in some way to improve efficiency in your telephony systems.

Right.

I imagine they'll do that rather than try to make these things bespoke.

So the challenge then will be okay, you buy something prepackaged, but you're probably still going to need some degree of expertise in house to tailor and fine tune and optimize that off the shelf product.

Yeah.

To your specific problem, to your specific customer or consumer base.

Yeah.

So I don't think the need, I don't think the need for that talent is going to disappear.

Far from it.

But I think the profile of that talent might be slightly different.

So there may be less build it from scratch and slightly more understand existing components and have the ability to optimize them to a particular challenge.

Yeah, yeah, that makes sense.

I wanted to talk a little bit about again, just the advice you'd have for other people.

So there's almost certainly lots of people that are in a similar position to you.

They're in either technical roles or they're kind of in those bridging roles between kind of leadership and technology.

It's often quite difficult to get that first chief data officer role or the equivalent.

Certainly if you're going from not doing that role to another business, getting that role, there's, there's, it takes quite a leap of faith for a business to take someone on in that position, especially given the nuance of the role and how new it is, etc.

So what, what would your, if there's kind of two or three bits of advice you would, you would give to, to maybe someone that wants to get into that role or they're in a business where they, they might, there might be an opportunity to, to step up.

Like what, what would, that, what would, what would you, what would you say?

Yeah, I, I, I, I first and foremost, and I joke with my team all the time about this, except I'm also not joking is enough and used Jerry Maguire as the jokey example, which is show me the money.

I think you have to have a very clear understanding of how you use data, data science, data engineering to drive value.

And you have to have a portfolio of experience that shows you know how to do that or at least you know, all the, the constituent parts that need to coalesce together in order to make that happen.

Right.

Because ultimately, you know, data is there to improve the services of the business and for the benefit of customers.

Right.

How are you going to measure it?

Is the question I always ask before we start anything.

Because if you don't know, we're not doing it right.

So you always have to have that mindset of always thinking about the outcome and how you're going to evidence that.

I would say that's 1, 2.

You have to really know and understand the business that you're either working for or aspired to work for.

And that has to be the thing that ultimately interests you.

Not just the tech or just the algorithm or just the tool that you have, that you're applying that sophistication to solve something in the real world and make a real difference.

And if you don't understand the problem, you're probably not going to build a good solution.

And you know that sort of falls into I guess my third piece of advice which is ultimately it's all about your ability to work with diverse groups of people and flex your style to be collaborative with them, to support them and to take them on a journey and help them understand how, how you can help them.

So you know you need to be able to demonstrate that you are open minded, influential, collaborative, supportive and you know you can flex very, very easily and you know you're able to when needed, say what you need to say in two sentences and get out the room or actually go into an hours long meeting and talk in the weeds about the details of someone, something.

But that, that is the only way you're going to build up credibility and the only way that ultimately people will be prepared to take that, that gamble on you and, and give you that freedom and autonomy to do your thing.

Yeah, and, and, and what about for you like kind of long term, what's the long term ambition?

Do you see, you always see yourself staying in the, in the chief data officer type role or, or do you see.

Because I can see a world where as data becomes more and more important and like you say the kind of combination of maybe a chief data and AI officer that maybe that turns into more of a kind of you have someone who's more technical, leading businesses, CEO, COOs, etc.

What do you think the evolution for you over maybe not the next year or two but long term.

Do you have a plan?

I could have never of anticipate but they're here now so I, I can't possibly anticipate where I'm at in the future.

I, I will say I, I, I genuinely believe that AI will mature in a way that will fundamentally in the long term shift the, the, the remit and the scope of what we all do.

Yep.

Both in our personal lives and, and in our professional.

I definitely think that AI is here to stay.

That there might be a trough of disillusionment in a few years about all this investment.

Where's the value?

I expect probably a degree of that but I think ultimately AI is here for the remainder of our careers and I think that will be more of a dominating influence in everybody's right down from individual contributor through to exec.

No matter what the letters are, you know, I very much aspire to, you know, keep on the bleeding edge of that as much as I am capable of, which might not be very much.

But you know, that's certainly where my, one of my big passions is at the moment.

Having focused on it for a couple of years now.

You know, McKinsey has us believe that 60% of all jobs in 2035 don't exist yet.

Right.

And I'm sure, I'm sure to an extent then that's probably true.

So you know, for me I've always been keen to learn.

I think adaptability is one of the most important traits in, in anybody's job these days.

I think as.

As long as, you know, as long as people are keen to learn and keen to adapt and prepared to, to do what it takes to do that, then you know, you're in the best position to exploit opportunities as and when they come.

And that's my philosophy.

Do you spend obviously coming from a kind of research background, you spend a decent proportion of your time still doing that kind of stuff in your, in your role now.

I mean since children a lot less.

So it's more reading up on Peppa Pig.

But no, I do, you know, as I say, mathematics is still a passion of mine.

It always has been.

Obviously I have a team of people far clever and I that.

That read papers and share papers and talk about that stuff.

I join in as much as I can.

I do read a lot in, in my own time to, to try and keep up.

Partly out of a need, a believed need to, but also partly out of the fact that I just enjoy it.

Yeah.

You know and I, I do make the time to do that.

Do you get most of that stuff?

Is that through reading you.

You.

You kind of read quite a bit rather than kind of watching other.

The videos that YouTube.

I mean it's probably erring on the side of watching stuff a little bit more now given the hours in the day I have around the aforementioned church was the same.

Yeah.

But you know, you know, whatever I feel like in the time that I get a multitude of different media.

Yeah.

I mean it's amazing now really isn't it?

I mean YouTube started off as this kind of thing that like kind of kids play around with to now it's like is this the wealth knowledge design?

Is that what I mean the explosion of the amount of resources for learning even to just of a decade ago is, is phenomenal.

I mean the challenge now as you touched upon before is that risk of information overload, of being sensible about what you do and don't spend your time doing.

Yeah, exactly.

I would.

I mean with podcasts, books, YouTube, et cetera, like you can, you could spend all day doing it.

Yeah.

They're never actually doing anything.

Right.

I've got some quick fire questions I want to throw at you.

So which is the.

What's the.

Your favorite, the brand or company you most admire?

I'm going to be really boring with my answer, but it is the honest answer.

Apple and the reason I've always had.

You shouldn't say.

You sure?

Oh, I had to give it.

Yeah.

I mean every iteration of their product has been seamless.

I know they spend a phenomenal amount of money on marketing, but it never feels like they have to shove themselves down your throat as a brand and that they, that they just are quality and everything that they do without needing to really shout about it.

Yeah.

Unless I'm just really indoctrinated by them.

Oh, I'm saying.

Yeah.

I saw an interview the other day.

It's really, it's a really good.

I'll share it with you.

Actually it's an interview like 45, 50 minute interview with Johnny.

I've about like the.

Because obviously he's a, he's a kind of, he's not, he's not involved in the technical part but from a design perspective which is what kind of leads a lot of the quality angle.

I think there's a lot of visionaries that.

Yeah.

But yeah, it's kind of just a plotted history of what he did at Apple.

It's, it's really interesting the piece of advice you, you wish you were given when you were first starting out.

Believe in yourself.

Yeah, yeah.

That just push, push yourself beyond what you think you're capable of and most of the time you'll realize you are more than capable of it.

Do you find that so like the teams that you've led and, and stuff like that and even in yourself that's, that's often where, where people's downfall is.

Just under us and you know, the risk of stereotyping, you know, I've seen it a lot in my experience, exceptionally bright academic people are often the most self critical and disparaging.

You know, they're in a way they're trained to be critical and critical thinking but you apply too much of that on your yourself, you start putting your self esteem at risk and that has all sorts of ramifications.

So I say, you know, the ability to lift people out of that spiral is actually very important.

Yeah.

Yeah.

Amazing.

If you could swap jobs with one person for a day, who would it be?

President Trump maybe.

I've said that to so many people on this podcast.

That's my one, my number one.

Not necessarily because I want to be him, but I'd just like to know what's going on in his head to be honest.

And actually.

Yeah.

Maybe get into.

Maybe stop a few things actually at right now.

But according to last yesterday he did stop it.

But I don't know about.

You never know.

Yeah.

Best.

Best kind of business or non fiction related book you've ever read?

The best but one.

One business book that's always stuck in my mind is something called Will it make the boat go Faster?

I mean I've always taken a lot of inspiration from elite sportsmen just because of the, the, the mental fortitude and perseverance and just the, the mindset they have to have to be top of their game.

And I think that that book just really taps into those traits which I believe are fundamental for strong leadership.

Really well.

Yeah.

Yeah.

I love that book.

We, in our business we use that phrase literally every day.

Yeah.

And funnily enough I restarted listening to it.

I'm.

I'm quite a big sauna go.

I saw listening to it the other day.

The fact that it tells like a kind of.

Of story.

Yeah.

And apply some logic afterwards in bits and pieces actually.

Really well written, isn't it?

And it's.

It's not just sort of like a self help book.

It goes through the lows and the highs and it's not just like a linear path to success.

It's about ups and downs and managing that, picking yourself back up and going again.

And it's real life as well, is it?

We should.

It uses the theory in real life.

Yeah.

Yeah.

Really, really great book.

Yeah.

The audiobook's really good as well actually.

The.

So if you can wave a magic wand and change one thing about insurance, what would it be?

I.

I still think there is a huge amount of work to be done to gain the trust of customers.

And again, that's one of the reasons that sort of compelled me to ensure that.

Just had a really strong vision on being a force for good in the industry.

Yeah.

I guess you see that more in personal line space.

Yeah.

Closest customers.

I mean we are insurance customers.

Right.

Do we really care about our insurer?

Do.

Do we really want to.

To, you know, spend time investing in having a relationship with them?

Do we really trust them in the event that something goes wrong?

There's still a lot of, there's still a big gap in developing that customer trust and a meaningful relationship.

Rather than just have some money I don't want to speak to for 12 months until it's time to think about this again.

You know, to coin a phrase, becoming more customer centric as an industry.

But that is, that is the magic one I would try and wave.

Do you think that's, you think that's in it is genuinely front and center in, in, in, in insurance business mind because you, it has, it has become.

I always talk about vitality as a business because I, I have my health insurance with them and, and when I moved left my last business, started the new one.

Sounds stupid.

I, I, I genuinely felt like I should stick with them because I think not only did they provide me with pretty good service, I didn't really use them much touch.

I'm still touch with relatively fit and I had to use it.

But, but the, the, the kind of the pot.

I've got a free Apple watch with the, the points and all that kind of stuff and I actually log into the app most days.

That's, but that's exactly it.

Right.

You're, you feel an affinity with them for those reasons?

I think it do, I think it's at the forefront of industry, different companies minds.

Yes.

But the litmus test ultimately is are your customers engaged with you, telling you they love you, you.

Right.

And until that's the case, you're not executing on that, on that objective successfully enough.

Yeah, yeah, yeah.

Penultimate one.

The person you most admire and why.

I mean again, it might be an obvious answer and a bit soppy, but you know, family, right.

Wife and children.

You know, we talk about my career.

It's, you know, it's not really been about me.

It's been about a huge network of people supporting me to get where I am today and backing me and sacrificing for me.

So you know, my wife is, is definitely that person.

What does your wife do?

She is a deputy head teacher in a university.

Oh wow.

A big, big university.

A big university.

So she has different challenges to me.

Did you meet her in, in academia then?

No, we didn't actually.

We met through a shared interest of music.

It's on us.

Yeah, yeah.

Nice.

Okay.

And then the last question is, what's the best thing about working in insurance?

I think the data assets and the volume of data that insurance companies have is unparalleled.

So again, from a, from a data science machine learning perspective, the ability to practice every facet of the discipline is there in abundance, unlike anywhere else I've worked.

So, you know, there's so many challenges and so many different applications of algorithms that can be used to very real benefit.

And I think that is the big selling point that is often undersold.

Right.

Look, thank you so much for making some time.

I know it's been difficult to kind of align diaries and what stuff, but we got there eventually.

So look, I'm sure there'll be some people that want to connect and stuff like that.

You.

You call without link to.

Yeah, phone only and so on and so forth.

So.

So look, there's we are taking a break soon for the summer, but there will be loads more episodes coming after the summer break.

So like comment, subscribe and uh, you want to contact Chris or I, then you know where we are and we will catch you next time.

Cheers Chris.

Thanks for watch.

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

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Sam.