What does a data driven strategy look like in the London market


Steve Jolley from Tysers explains his approach to data and analytics, what a data driven strategy looks like and where do you start?

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Steve Jolley:  So what does a digital strategy, sorry, data driven strategy look for us?

Well, first off, it means different things to different folks. Same is digital. So for me and for what we're trying to do at Tysers.

We were developing a strategy and we sort of got a nominal North Star in terms of what we want to achieve. Our CEO wants us to be a data driven digital broker. So for us, what does it mean? It's about basing our business strategy on data. That's growth, that's profitability. So, what's our total cost of ownership? As broker, where are areas of efficiency that we could we can maximize? Where are areas of inefficiency that we can automate or stop? What's our business target operating model and how does data drive what that needs to look like? And then typically, which is a bit 101 is, as a broker, what are we doing in all regions? What types of business are we doing? What classes of business will be doing, you know?

For me and for us, data and analytics is about outcomes, it's about evidence, it's about stuff. It's tangible, it's fact based, it's not emotional. So traditionally brokers, and I've worked for some in the past, based a lot of their business strategy on emotion, on non facts, on things that they think are appropriate in terms of growth without necessarily understanding where we are strong, where we are weak. So for me, that's what a data driven organization should look like and.

Jason Cripps: Steve, I do agree with that. I mean, I agree with a lot, obviously. But how do you decide what what is the right data to start to use this start up piece because you've got a mass of information across the organization?

Steve Jolley: Yeah, great question. So yeah, you got to start somewhere, right? Because you can bury yourself in. So we took a quite a basic, a crawl, walk, run perspective. You know, we we invested in a data team with analysts and engineers. I thankfully walked into a business that had some capability in there, but we needed to add to it, we needed to get architecture. We needed to add some reporting and management capability.
So where do we start? We started with going out to see our business lines, our MDs and our front office guys and asked, "what do you want?" You know what data will help you run your business better. We then obviously look to our to back office as well in terms of what data do you need in order to operate more efficiently to understand where your pain points are, etc.

So we gathered some evidence, we understood what we what they wanted and then we went away to them. Find out what have we got? Where is it? How can we bring it to life? How can we start to then surface it, standardize it? In broking house data dictionary is so valuable for us. You know, you've got fees, you've got commissions, you've got brokerage. They're all income and they all mean different things in different regions. So getting an idea as to what that is, Jason and then starting to build that profile up to so you can, you know, that's the crawl that right? So I mean, that's the you can start to report. That's more our MDs and they can understand where they're strong, where they're weak, where they're efficient, where they're inefficient.

Roger Arnemann: I think it's also sort of interesting when big data came out and people said what are you doing with big data where do we put it? And it's like, it's like a hammer looking for a problem right now. Everyone's like, Where can we put the data? How are you going to use the data? And you're like, Wait a second, like the objective here isn't use data. The objective is run our business and there are relevant parts to use data. I think most people will find those relevant parts are very large right and end up becoming a data driven organization. But I think some people put their hand up and say, we have to be data driven. Here's all the data we could buy. Where do we use it? And again, that's a hammer looking for a problem? I think the better way to say is, where can we improve in the organization? What issues are challenging us? What opportunities would we like to grasp that we can't grasp yet? And then how do we unlock those? And very often you'll find data analytics are those unlocks

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