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Setting an insurance business up for the future, with MS Amlin’s David Grainger
Episode 827th June 2022 • Fibonacci, the Red Olive data podcast • Red Olive
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David Grainger is as reinsurance statistician with MS Amlin, an insurance company that is a leader in the property, marine and reinsurance markets. He has worked in the IT and data space for several years and has been at MS Amlin for the past 12.

David joins us on this episode of Fibonacci, the Red Olive data podcast to talk about the challenges around data in insurance in what is a very conservative industry.

We also discuss where the opportunities are for businesses like MS Amlin that want to use analytics and insights to future-proof their technology investments and bring reporting and business value to the entire organisation.

Here are the topics we discuss with their timecodes:

  • The challenges that the technology teams have to keep up with the pace of change in the business (2m 53s)
  • The journey from disparate systems to a single, enterprise-wide platform (6m 6s)
  • Difficulties in delivering a single platform when there are many individual programmes going on (7m 59s)
  • Dangers of projects that are delivered in silos, and a ‘business as usual’ approach (8m 49s)
  • Becoming a data-led business when you have multiple, business intelligence tools (12m)
  • How the hub and spoke model works for the organisation (14m 26s)
  • Tying the Wherescape, Infosphere and reporting platforms together so regulators can see evidence that there is good governance of personal data (14m 57s)
  • Improving fraud catching capabilities (17m 11s)
  • Becoming leaner to be fitter for the future (18m 22s)
  • What to do when programmes don’t deliver their aspirations (20m 27s)
  • Moving to the cloud, future-proofed by the Wherescape tool, and working with Red Olive (21m 33s)
  • The effect of Brexit on the business, and moving data when the data is in multiple systems (26m 51s)
  • The skills a data professional will need entering the industry (30m)

Transcripts

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Hello and welcome to Fibonacci the Red Olive Data Podcast all about data

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and analytics, where we hear from leading specialists and get their take

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on the industry. I'm your host, Nicky Rudd. Today, I'm joined by Dave

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Grainger, reinsurance statistician at MS Amlin, a leader in the property

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and casualty Marine and reinsurance markets. Dave has worked in the IT and

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data space for several years and has been at MS Amlin for the

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past 12. We talk about the challenges around data

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and insurance in what is seen as a very traditional industry,

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but also where the opportunities lie for businesses like MS Amlin, who want

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to use analytics and insights to Future proof their technology investments

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and bring reporting and business value to the entire organization. Let's

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find out more. I just wondered if you could tell me a little

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bit about how you got into the world of data.

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I stumbled into the world of data back in

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probably 1990 1991. I started a job in insurance, in the insurance industry

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when I was 17 as a accounts account clerk. And as is the

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way in insurance businesses, I worked for a very small company that got

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bought out by a large motor syndicate back in 1990.

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I'd started to be the computer person in the accounts department so I

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was playing with the PC that had been installed, obviously all had green

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screens in those days. And my boss put me forward to the IT bar, it

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wasn't called IT in those days. It was called data processing.

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I'm that old, right? So I got put forward to just speak to

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the head of that department and my boss, Jerry Burcham at the time

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that I interviewed with him, he was a massive cricketer as I was.

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So my interview was pretty much about how fast I bowl and what

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number did I bat, etcetera. So I got my job through sporting connection

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really and really enjoyed working with him. He really sort of shaped my

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early part of my career with a lot of young men.

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We worked in a computer process department, probably had about 40 young

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men. I mean, just to give you an idea of how old we

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were. One of my good friends at the time Simon, he was 25

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and we named him old chap. So just gives you... Quirky. An idea of

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basically 18 to 22 year old boys try to learn how to

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develop computer systems. And I had an actual

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affliction to reporting and just started building reports for the finance

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team and then started to build out stats, databases, etcetera, from there.

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And so that was my sort of first introduction

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into data from what it can do for businesses.

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You've been at MS Amlin for a while and done this huge reporting

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and re reporting sort of project. Do you want to tell us a

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little bit about that? So I've had a long career at MS Amlin. I've

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been there for over a decade so when it comes to data,

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I know where most of the dead bodies are. So yeah, there's lots

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of challenges with systems so they start off

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with a great intention, whether that be an insurance application to store

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data or for platforms to build data. And unfortunately,

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I think one of the biggest challenges with data is that the technology

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teams struggle to keep up with the demand of change that the businesses

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want to do. So a new class of business wants to be set

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up out by the business to do that properly on the platform,

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the IT teams will put a proposal together, spend

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weeks prepping that. And obviously a lot of the time we've experienced over

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many years is that budgets just aren't there to do

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platforms as well as we should. And therefore they go further the quick

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win to get the business classes up and running. So what that means

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is that they hijack the system. They'll put some business logic around the

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system that doesn't exist in the system. So they'll prefix a policy with

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a specific code to therefore, they will know what those policies are but

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the system doesn't the downstream platforms don't etcetera, etcetera. So

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part of the landscape that we've been learning through and evolving is understanding

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those things and trying to transition that into the reporting layer layers,

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so that everyone gets that single version of the truth. Amlin has as an

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organization that's has gone through many big changes over the last 10 years.

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It's gone from independent legal entities that are master of their own destinies

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reporting into a group PLC business to the group PLC business reshaping

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itself, to have more control over those legal entities and bringing them

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together to get best of breed across their

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insurance entities. And then subsequently at the end of that process,

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we are bought by a parent company MSI and they, just because there's no

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longer need for the PLC business that got disbanded a couple years ago.

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So now we're back to the legal entities being in charge of their own

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destiny, again, in that sense. So we are going from a sort of Constantina

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of a data world in that we, we bring data together for group

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wide reporting. Now we're splitting it apart for legal entity reporting.

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I'm sure at some point that they'll come back together again,

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right? So we have all of these challenges. And what that means is

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that we have lots of despair underwriting systems and operational systems

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because people make their own choices about what they wanna do for their

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business. And they'll share information, especially when we look at our

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re insuring programs where we wanna re insure our inward risks.

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So we still need to share that data.

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So therefore try to build a reported report data architecture platform

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over that landscape is very complex, time consuming

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and ultimately very expensive. So they are the things that.

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We've learned over the years. I think most data professionals know these

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lessons. It's just actually having the time and the team sizes around you

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to do the best practices. I would say we're 50% of the way

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of our journey to get to that single

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enterprise wide platform that can be trusted and used by all, all users

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of that data. Be it that individual legal entity or

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partnerships between the legal entities to share data. So we've made some

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good progress over the last two, three years, especially with, with the

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Red Olive business, helping us on some key projects. But yeah,

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we've still got quite some way to go. Obviously it's massive and it

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is changing, but where do you typically start with the project? And do

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you have your own way of thinking about it? Like you say,

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you've learned over the years and sort of honed down from mistakes made.

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And I always think that it's, that's that difficult thing of actually having

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the end goal. what do you actually want it? And finding out from

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all the different stakeholders within the business, how they would use the

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kind of information that the data shows, but do you have a process

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that you've now adapted so that it works, or, you know, what's your

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approach? I think we're, we are evolving our processes. So again,

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because of the disbarment of the PLC business in the last two years,

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I was part of a, what was the business MI function,

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which was, you know, was a PLC function. So it was a

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group wide reporting function. So with the disbarment of that PLC business,

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we've now evolved into essentially having data functions within each legal

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entity. And so we are all learning. So parts of that team got

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federated out into the separate legal entity teams. We're all still evolving

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out ways of working. But I think the

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challenge you have with data programs is, you set out your vision around

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what does the end goal look like? And I think with individual programs,

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that's very difficult to do. I think the lessons that we've

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learned, I think from the recent programs we've run and we are, we're

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starting some more at the moment is that individual programs contribute

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to your data architecture landscape and you may have a, an ultimate vision.

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So yeah, from my perspective, I'm responsible for the business intelligence

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tooling and the reporting platforms that support those across the organization.

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So I only want one business intelligence tool to only want one reporting

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platform because therefore that's cheaper, easier to maintain. It could

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be focused on supporting those platforms much easier. At the moment we've

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got multiple business intelligence tools, we've got multiple platforms,

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projects, and programs deal with those individual things. So they're gonna

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contribute to our ultimate vision of having single platforms that can be

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used easily, etcetera. So I think starting up

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projects and all programs, we're starting to really drive out data piece

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as early as we can into pits bits, etcetera. So we get programs

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and projects talking to the right people. I think that's the key thing.

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So typically you having big projects and programs is that we need support,

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we need support from third parties. So we'll, we'll bring in a partner like

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Red Olive that will have the right skillset to help us build our

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platform, but that not necessarily have the understanding of the data landscape

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Amlin has. So that's a very difficult thing to pick up first and

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foremost. So if are not careful, what tends to happen is that projects

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and programs tend to work in silos, 'cause they're delivering their specific

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part of the jigsaw puzzle. And without knowing the Amlin data infrastructure

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and how things work necessarily, you can look at data structures in our

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database and see exactly how things work, but actually knowing the nuances

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of how that data's actually being used is a real challenge.

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So from my perspective, the great thing that I've got from working with

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Red Olive, especially their lead consultants, Mark Fulgoni, David Searro

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is, our engagement model with the stakeholders across our organization.

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What does that look like? Because we are a business as usual team.

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Too often, we just get you know how to do stuff.

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Can you just do this for us? And then we'll go off and

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do things very quickly and that doesn't ultimately build a proper relationship

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out with our, with our stakeholders. So we're, we're now really evolving

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towards detailed engagement requests. So they understand the full implications

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to what they're asking, because just add that thing, that table,

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that field to the platform will be fine. Isn't necessarily the right thing

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to do. So, yeah, we've got lots of learning to do,

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I think, as ever, we never have enough people to do that.

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So that's another, it's another challenge for us is

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how we build up our internal teams again, to support the demand and

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be able to really articulate how that benefits the business. Obviously within

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the insurance base, it's seen as very much a sort of, I would say,

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traditional business in the way that actually, you know, sort of data flows

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and yeah. How an organization is sort of set if you like,

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how have you managed to sort of pull everyone together into thinking differently

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in this kind of new way of working? Is it challenging them upfront

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about get what they want the data to tell you? Is it a

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communication to sort of, a bit of education? We're very much at the start

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of our data maturity level. So the insurance industry is typically slow

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moving toward that change. So we have a lot going on,

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but it takes time to do that. The RFS 17 accountancy regulations that

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are coming in that's been a big, slow moving beast, thrust to move

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towards. Our legal entities, have aspirations of being data driven companies.

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They're very aware of the disruptors in the marketplace and they really

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want to evolve. There's some really good initiatives going across the organization

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around robotics and data science. Our data science team are doing some good

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stuff with specific parts of the business in our London digital hub.

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So there's definitely the right steps forward. I think the challenge that

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you have is that you have to have the platform in place so

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that you can support the business so they can take that journey.

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So too much time is being spent to actually just deliver the core

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foundations I was talking about around, we've got multiple business intelligence

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tools that some people still have to rely upon because not all the

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data's available in other reporting platforms and other business intelligence

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tool. So we need to get that structure in place and foundations in

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place for them so that we can accelerate that data led culture.

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Right. Which is what everyone is crying out for in our organization.

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How do you do that in such a sort of large organization,

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so that whole digitization and affecting the industry, as well as kind of,

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other regulations and compliance, sort of guidelines and things like that.

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Do you break it down into steps or have you got a kind of, we've

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got a 3 year plan or? I think hope that's a really good

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challenge because obviously we've got the legal entities having their own

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business change initiatives. And I think that's where

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the, criticality is for us right now as an organization is that we

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need to ensure that we've got the right people

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plugged into all of those projects, because some of those we completely

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driven and led by the business and it's making sure that we've got

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the right people with touch points on there, so that they can steer that

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in the right. So we make sure that we get the best benefit

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of those initiatives into our data architecture platform.

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I think it's quite easy for the business teams to just drive forward.

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They see these things, they want these things,

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we've all got handheld devices that have got

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so much information at our fingertips. So why can't we have our insurance

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data at our fingertips, right? So that's not a too difficult service. It's

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actually trying, the cell is actually doing it in a constructive manner

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so that everyone understands where that single source of the truth is.

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So as far as how we are doing at MS Amlin, we've got,

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as I said, local data functions in each entity. So we've now got

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a hub and spoke model around the data organization. So

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we've got these data functions that are responsible for an interesting point.

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You said around the regulatory demands, that is becoming more and more stringent

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and will continue to do so. Just seeing how the banking sector's gone

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over the last decade, I'm sure insurance sector will follow there them as

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well. So it's really critical. I think for us to be able to show

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that we've got control over our data. So there's been some really strong

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project initiatives over the last 18 months around data lineage, data capture,

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data management, and they have been at the forefront of

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how we tie everything together. So how we are doing that as an

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organization? I think we've had some really great support from Red Olive

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to show us how to do that in a better way.

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So as you'll know, the west cape CAP platform that Red Olive are very

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skilled in. We've used that to help us support our, data and image. That's

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given us a good starting point out of the box. We've got,

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really great data capturing, info fit all and IBM tool, which has got

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our business glossary and data glossary in there. And what we've been able

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to do is using the skills of Red Olive is be able to

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tie that platform up with our reporting world. And we're really starting

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to evolve our reporting platforms from a regulatory perspective to actually

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showcase that we do have good governance over our data

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so that when we do, returns to the regulators will be in a

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position. So when they come and say, "Right, show us your data controls"?

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We'll be able to do it in the future as a push of a

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button. And it'll just be part of our processes. There's obviously like

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the current what's going on, but there's also this sort of,

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like you say, the future of when you're gonna be asked stuff. And

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also you've got the kind of, sort of big trends in technology that

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are sort of pushing with the emergence of machine learning and,

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sort of applying data to solve problems. What do you see as some

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of the big areas of opportunity to apply machine learning in the insurance

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sector? Is that something that you are currently looking at as you mentioned

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earlier about some data science team doing some interesting projects. Is

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there something you could share on that? I certainly think that's definitely

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on our radar. I'm not so sure specifically about machine learning at the

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moment. I know there's been some really good used cases in the claims

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space, certainly in the, in the insurance sector.

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We are obviously always looking at evolving our processes.

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One thing that I know that we are starting to look at is around

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our fraud catching capabilities. So I think we're looking at seeing how

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potentially advanced analytics can help us identify fraudulent claims quicker.

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I think that's probably gonna be the best space in the immediate term

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for us to, look at that. But yeah, I think the data science team

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have been doing some good work around looking at our losses and our

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risks that we right. So they've been looking at how we can protect

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ourselves better with better information. So we now have a modeling tool

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on our Marine business that checks where the vessels go, that data's all

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available the maritime data. So we've plugged that into our risk models

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and they've done some really great work with the Marine underwriters to

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capture more premium is probably the best way to describe that.

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Where we, where we're finding out that vessels are visiting excluded territories

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so they don't go into, but they do. Right. So that's been some

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really insightful stuff that the data science team have done.

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I think from our machine learning perspective, I think once we've got our

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platform more organized and consolidated, I think that then allows us to

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better make use of that type of technology.

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Where are the biggest challenges and opportunities with the data and extracting

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it, because I'm guessing that picture's building all the time with the amount

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of data sets that are coming in and the more information you're collecting.

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Yes. I think that's a really interesting point because obviously the businesses

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are going through a lot of change at the moment, either through,

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performance re evaluation. We've had some tough years as a, as an

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insurer and reinsurer, certainly in the last five years, it's been a very

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difficult marketplace for us. So there's been a lot of consolidation going

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on. So we are I suppose getting ourselves leaner is probably the best

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way to be fit for the future. And I think we've got these

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big programs like RFS 17 that are preparing us for the future.

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So we'll be looking at underwriting our books of business in a different

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way. Once that's settled down, going into I think next year is the

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first year that we'll start reporting on RFS 17 basis

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that will then allow us to look at opportunities. And I think the

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key thing is the growth that the entities will be looking at.

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So to do that, I know we've got

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as an example we've got some online platforms coming online this year and

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plugging those into our backend architecture is gonna be a key piece,

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certainly from our financial and reporting perspective. Once you've got

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that in place, then our analytics can then start to go to work there.

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So there are lots of challenges. I think that, is ensuring that our platforms

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can keep up to pace with what the business wants. The data function

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and the IT teams will always be behind the business decisions because some

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of 'em will be hugely confidential. IT teams won't even know

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that there may be a small merger on the card or an acquisition

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that's happening. But I think the platforms we've got in place at the

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moment do allow us to onboard data as quickly as we can.

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So there's lots of interesting scope, I think that's coming down the Pipeline

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next year. Would you say that, just being able to be a bit

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more agile with those platforms is kind of opening up opportunities or maybe

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managing frustrations where in the past perhaps the IT department, would've

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specced out a system that even if it wasn't fit for purpose after

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five years, for example, you still had to get the ROI from that

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investment? Do you think that sort of changed in the last few years?

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I think that's a really good question. I think obviously there's always

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challenge where things don't deliver the whole scope. You consistently see

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big aspirations with programs and this is why I was saying to my earlier

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point that programs shouldn't be drivers of aspirations. The aspirations

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should be there and programs and projects should contribute to that roadmap.

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And I think too many times programs try to do those things by

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themselves. And I think the key thing is having the tooling available to

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be more agile around data ingestion, et cetera, but ensuring that it's done

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in a strategic way. So one of the reasons why we'd like the

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WhereScape platform, the tooling when we selected it a couple years ago

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is the fact that it's essentially technology agnostic in the... We've got

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aspirations to move to the Cloud, we are a SQL server shop at

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the moment. And I think there's definite aspirations for us to get to

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the Cloud using the WhereScape tooling. It means that my team only have

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to learn one tool and we can then deploy.

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It's not gonna be seamless and it won't get everything right in one go,

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but certainly the bulk of the platform will be created by the tooling. And

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then we just be left with the tricky 20% just to get right.

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So I think that future proofs our data platforms that we can move

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with the times. And if someone decides that the Cloud is the best

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way to go for everything that we do, then we don't need to

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go and reinvent the wheel in the Cloud. We can get there certainly much

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quicker. And I think the Cloud is certainly something that enables us to

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be more agile in the future. We can stand up proof of concepts

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much quicker going forward. Obviously you mentioned about Red Olive and

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working with them and WhereScape being kind of a key tool for you. You've

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obviously been working with them for a couple of years, I think.

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Was it useful having external consultants coming in and challenging the

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way that you were thinking? I'm not so sure they challenged the way

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we are thinking. So my experience of working with Red Olive was that actually

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we were quite aligned in what we are thinking. It was actually the

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execution. The platforms that we've built so far with the WhereScape technology

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used to support our financial reporting. That is

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a guy heavily normalized database at the heart of our

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data capture as you'd expect, it needs to be very strong 'cause it's

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dealing with our financial transactions, it supports our general ledger,

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it supports actuary reserving process. So that platform itself is great

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at what it does. And obviously it being highly normalized means that it's

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not great for reporting and analytics. So we needed to build out reporting

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marks that would support easier reporting. When we engaged with Red Olive,

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initially it wasn't a case of showing us new things other than actually

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look at this tool that we can deliver traditional reporting marks we were

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used to working with via this tool and we did a proof of concept

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that was about a week long. The biggest challenge of that week was

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actually getting people logged in into our platform because we were in lockdown

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during COVID. So that was a real challenge. Just being able to get

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someone to log into our network, to connect to some of our data,

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but I had a very light touch on the POC, but

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Mark was able to demonstrate how quickly the tool could help us transition

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highly normalized data structures into more typical star schema, data dimensional

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modeling structures. Very quickly, they built in documentation of the WhereScape

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platform and the data used in tracking made it very, very appealing as

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a tool to move forward. The tool itself, when I was looking at

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it, I could probably write the code and my team could write the

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code it generates just as quickly but the key thing was really around

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the documentation. The data linears tracking, ease of use of the tool was

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quite a key thing as well. So it was definitely a put your

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ego aside of, well we could write this code anyway. But that tool can't

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write code as well as I can but... And it's not magic.

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It doesn't do everything you'd wanna do it out of the box,

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there's always a nuance with tooling, but for me, I was really sold

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on the ease of use of the tool and how quickly Mark was

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able to demonstrate that. Now, obviously Mark's very experienced WhereScape

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consultant. So not everyone's a Mark Fulgoni. But for me, it was a

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key selling point of that platform and how we can make use of

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it going forward. How quickly could you see the value of implementing the

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system? Or is it kind of a very quick...

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That you were running before we working it that fast?

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It was literally at the end of the POC terms a week.

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I was like, I'm sold on the benefits the tool bring.

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The project it was about to embark on was a challenging one anyway.

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Again, to come back to projects and stuff being run in isolation with

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other things that are going on is always a challenging space.

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So the platform that we built together took longer to do than was

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first thought because I knew it was a very complex project,

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but other people hadn't really grasped the fact that it was a challenging

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space we were working in. But I think the tooling is great.

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It's making sure you understand the pitfalls of where you're starting what

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are your data sources, et cetera, how complex are they?

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Does it have all the data in the platform that you're sourcing to

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build the analytics that you need to. Now One of our challenges was,

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as I said before, we've got data in many different reporting platforms and

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data sources and business intelligence tools, so not everything is in one

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place, and that's the journey that we're still on.

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On a completely different note, London's Financial Services Center was a

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big point of discussion in the run up to Brexit, I kind of mentioned it

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with all the political shenanigans going on at the moment. As someone working

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for a London insurer, do you think Brexit has made much difference to

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your organization? And if so, what? It certainly cost us a lot of

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money as a first point. So as part of the Brexit deal,

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we actually had to transition all of the risks that we've written in

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Europe to Lloyds Brussels. Lloyds of London set up a Lloyd's Brussels office.

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Now, I don't know all of the insurance side of that,

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but by law, we had to transition all of our European list from

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Lloyds of London to Lloyds Brussels. So yeah, we had a dedicated part

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seven projects as part seven is actually the Lloyds contract, I believe,

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that did the transition. That's been a really challenging project. Again,

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we come back to our data is not in one place,

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so therefore, how do we get all of that data? So

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very briefly, most of our European business that we broke was through a

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property and casualty business in the UK that used

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four systems for its underwriting. So getting all of that data out of

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those systems in one place in a format that Lloyds platforms could consume,

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it has been a very challenging space, and it continues to be today.

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So I think from a actual insurance perspective, from a business level, it's

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probably not been too traumatic, but from a technology perspective, it's

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a real challenging thing to do. And obviously, it was all new, right? It

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was new for Lloyd's to understand what was needed, so we had very

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evolving timelines and requirements. So requirements would change in the

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Lloyds marketplaces. Obviously, it's a great place to do business, but

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all insurers and all brokers do things slightly different. Again, there's

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not one single Lloyd's market platform that everyone uses, insurers and

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brokers have their own. So trying to find that common ground around how

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insurers write their business has been a challenge for the Lloyd's platforms

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to receive. But I think the feedback I've had from certainly the project

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team that was working on it, the collaboration between the Lloyd's syndicates

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was really strong on this initiative because no one's ever done it before

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and they had tight deadlines. So it's January 21 was the drop down

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deadline date that this was happening, and we only had

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effectively a year to do it. So it's been a real challenging

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space, but so far, it's been pretty well done. So apart from costing

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a lot of money, I'm not sure what the benefits of Brexit were for

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the insurance industry. I was gonna say it's still not mentioned within

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the office, the V word. No. Okay, well, we started off with how

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you got into the world of data. I just wanted to ask if

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somebody is thinking about coming in, finishing uni, or they've decided

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that actually data is what they want to be doing. What advice would

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you give a younger data professional starting out? What skills do you think

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they should be kind of honing, and what kind of things do you

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think they should be looking to do? It's a really good question.

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We've had some graduates that come from another third party that have onboarded

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into Amlin in quite recently. So I've spent the last couple of years,

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they've been supporting us on the reporting project as well. And I've been

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talking to them quite a bit actually around

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what do you like doing? What are the things that you wanna do?

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And I think that it's a really interesting space, data.

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The first thing is, do you love it? Because if you don't love

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it, then it's a difficult, boring space to be in. But I love

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data. I've just grown up with maths in my head as a small

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boy, so I played, as I mentioned earlier about my first sort of data

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or IT job interview. I love cricket and football, so sports.

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There's tons of data in sports, especially cricket. Coming out of uni, one

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of the things to go for, so I think there's

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the cool fun stuff to look at So you'll hear machine learning,

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you'll hear python, you'll hear... But actually just understanding data

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structures and understanding the business domains that you're working in,

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that goes a long way. So the technology side of data is one

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thing, but understanding the business domain you're in, I think is the key

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differentiator between being just a person who builds and does reporting

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as an example or someone who can actually make a difference and take

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a lead in the organization you're working is really understanding the domain

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knowledge so that you can take your skill sets you've got from your university

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courses and stuff, and you'll be able to run with it. That's my

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main advice, get to know that domain knowledge, the business teams understand

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themselves. I think that's a really valuable piece of knowledge because

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technology changes and evolves the whole time, but actually if you can use

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that as a tool to actually see the bigger picture. Yeah,

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no, I think that's the key thing. You can learn a new technology

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skill. Even I, as an old man have learned some new stuff in

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the last few years. WhereScape Red, if you've got lots of technology skills,

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you're able to just fit technology and understand it to the domains you're

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in. So you really need the domain knowledge to help you do that,

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in my humble opinion. Some interesting points there about setting some objectives

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for your data projects at the outset and using tools to help,

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not hinder. Join us for the next episode of Fibonacci, the Red Olive Data

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Podcast, where we'll be joined by another data expert sharing their thoughts

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on the latest trends in AI and big data, along with some great

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hints and tips. Make sure you subscribe to Fibonacci, the Red Olive Data

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Podcast from wherever you get your podcast to make sure you don't miss

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it. That's all for today. Thanks for listening. I've been your host,