Jon: So Tom, did we go to Australia? What was the motivation for doing a three week trip ⁓ the entire Australian continent?
Tom: Welcome back to Transform. joining us for part one of a three part conversation John and I's ⁓ journey across Australia.
Tash Wilson: You've been listening to Transform by GHD. Big ideas, real impact. Welcome to Transform, tomorrow's thinking today. Real conversations on the innovation shaping businesses and our society.
Tom: I'm Tom Biederman, business group leader for Data and Insights at GHD, ⁓ joined by John Jarrett, leader for Data Insights at GHD. ⁓ What were your key take homes that discussion?
Jon: I'm not sure one specific right? But it was ⁓ amazing to talk to so many clients. And I think the ⁓ one thing that did stand out more anything else was the range of maturities ⁓ across the data information and technology space that base in line with what you would probably expect in terms of ⁓ different ways in which they're funded,
Tom: ⁓ so John, we GHD back in January 2020. And since that time, we've of built ⁓ a data advisory and team ⁓ in the UK. There's of around 40 of us ⁓ now working across a range of different infrastructure types. So some quite exciting projects, ⁓ the kind of scale the projects has been increasing. ⁓ Yeah. Obviously, GHD in the UK is quite small. There's about 350 people in total, of which we're beginning to be a kind of large-ish proportion. But then the question was kind Australia, obviously, GHD has got a larger presence. But the kind of data advisory, kind of direct to clients is quite a of a thin layer. So is there opportunity for us to be kind of serving clients better?
Jon: scopes of different business models, etc. So ⁓ we saw everything ⁓ privately funded clients in the private space around freight and ⁓ tolling that were more advanced than the cutting edge in infrastructure space here ⁓ in the UK. And ⁓ government funded with very and thin budgets in that space that were
Tom: Yep. Yeah. by ⁓ bolting on data services or providing data infrastructure services direct into clients. So a bit of a fact-finding mission really. And it was really good. had three weeks there. ⁓ It was really good to have those.
Jon: but still to with ⁓ the same of fundamental issues ⁓ as better clients. So really impressive work being across the piece, really, ⁓ that level of maturity
Tom: those discussions really good opportunity just to kind of, asking clients like, where are you with your kind of data, your systems for managing your infrastructure? ⁓ And also an opportunity for us to kind of bring over kind of you know, where is the UK industry at the moment? What's working? What's not working? What are ⁓ the priorities of different clients ⁓ and that kind of thing. What are the biggest challenges that we were seeing and what are they causing to those clients?
Jon: of resilience in the basics is an answer that essentially ⁓ out a number of different ways. And it fascinating to talk through the number of clients that were dealing with a lack of clarity in their data and real basics around does the data mean? What do we mean when we say a bridge, when we say a road, when we use individual terms, particularly clients that were trying to bring together multiple legacy systems that covered the same data domain, so multiple systems that covered property of organisational mergers, ⁓ Data was an issue that probably as always came up, ⁓ although interestingly saw both.
Tom: Yeah.
Jon: clients capabilities the marketplace that were really kind of much more focused on resolving data quality issues than we see here in the UK. ⁓ And some clients that have really gone the extra mile build the business case for data quality and to resolve it, which is really interesting and a ⁓ super interesting reflection for us kind of coming back to the UK.
Tom: Yeah, yeah, that definitely stood out. Yeah, kind of stands out how the kind of industry dynamics are very different actually UK compared to Australia, ⁓ mining as an example where you to keep your assets going. then therefore, it's worth investing in the systems, but the high quality data in the systems, ⁓ which you often don't see in the ⁓
Jon: and Yeah, absolutely. Yeah, resource extraction was kind of the sector that has really managed to make that connection between underlying data and end profit, which is, which is obviously very difficult for a publicly funded railway to do. ⁓ But seeing kind of next to each other, literally ⁓ in the next office in ⁓ in some
Tom: Yeah. No,
Jon: the difference in those business models and kind of the impact that that had on their ability to deploy data information technology into the asset management and the asset lifecycle space was really fascinating.
Tom: Yep. Yep. Yep. Would you say everyone had a kind of vision as to where they were going?
Jon: I think as ever with technology, people use it in their personal lives. People see how things are moving and particularly now we're ⁓ fairly into the AI revolution. People want that and they want to deploy that. I'm not sure it's ⁓ as coherent as a vision, ⁓ there's a very clear and well understood thread of we know we should be better, we want to be better, ⁓ we want to go there as a business. ⁓ think what was interesting was ⁓ that were really succeeding and really making kind of super impressive progress in that were the people that had connected that desire to be better.
Tom: Yeah, yeah.
Jon: to the underlying business model of their businesses and the organizations that were struggling and long-term about actually improving and deploying ⁓ were where that connection to the business model was understood to be weak.
Tom: Yep. And I think maybe a common theme across the UK and Australia was that kind of growing understanding, like technology is becoming an operational cost. So it's kind of an operational aspect of the business, isn't it? A lot of investment over the last kind of 10, 15, in some case, 20 years, but it's not static. And to keep it going, it's investing in the data of your infrastructure. And that kind of builds you on the journey towards more and more.
Jon: Yeah, absolutely. Yeah.
Tom: insight about how your assets are operating.
Jon: Yeah. we're struggling to understand whether ongoing investment in data quality, ⁓ is required, ⁓ understand just thermodynamically, we can't leave this stuff. Entropy is going to come and get us. ⁓ data is going to continue to degrade. It needs be up kept. But if we haven't correctly understood that that is OPEX and needs to be funded as OPEX and there's a key part of
Tom: Yeah.
Jon: delivering the service on a day-to-day basis and kind of achieving those day-to-day objectives. And we're still treating that piece as individual slices of capital investment. that's ⁓ a challenge because not capital investment, ongoing operational costs. ⁓ And getting that aligned, think, was clearly a challenge ⁓ some places. That's not say that the UK is brilliant at that, but I think there are certainly for of the infrastructure majors here ⁓ the UK, the with the regulator has enabled ⁓ it to be clear that that is OPEX. So it's not necessarily super strong connection ⁓ to the business case, you've got the presence of a regulator there that says, no, that's OPEX. And ⁓ we all accept that that's OPEX and we're on that basis.
Tom: Yeah, yeah, yeah, so there's a nuclear Yeah. Yeah, yeah. But even there, like in the UK, still finding its feet on how to address that, I would say. And if you roll back a few years, a few more years, like 2018 but ⁓ organisations be building their kind of like in-house ⁓ data, science teams as their kind of solution to that. But then you've got to kind of keep that going. ⁓ There's kind of talent management aspect to it and those kind of things. And then it's actually realising that
Jon: Yeah.
Tom: some of it is kind of more like moving as the systems and products are moving and making use of the tools as they're coming which is what you're paying your license costs for anyway.
Jon: Yeah. Yeah. it's immensely for an infrastructure organisation to be as agile as they need to be in that space, right? ⁓ a a big tech major, they will chop and change ⁓ the focus of their what they're doing, what they're investing in on an almost weekly basis when things are moving fast, ⁓ daily if required.
Tom: Yeah.
Jon: You can't do that in regulated safety critical business. can't taking an axe to the org structure ⁓ every week. ⁓ It be chaos. So you to balance that safety critical requirement stability and ⁓ allowing people to what is happening on a daily basis ⁓ with the need ⁓ refresh and continually ⁓ the systems piece, data integration piece, ⁓ your analytics piece.
Tom: infrastructure.
Jon: AI disrupts entire flow in some way, shape or form ⁓ in that ⁓ again a lot of thinking about and ⁓ a of careful planning to get them right, particularly if safety is involved.
Tom: yeah. ⁓ what would we kind of say as a simple set of organisations can be taking to kind of clean structure their data so their decisions can become ⁓ and more auditable, defensible and valuable.
Jon: I think the first thing is sure the business case is there, right? Like understand what is data quality doing on the front line of the business. And I think, a couple of clients out, naming their names, of course, but like a couple of clients stick out where they clearly understood that a visceral identity level, the poor data quality. was significantly hampering their ability to execute on a daily basis, but they could not attach a dollar sign to that. And that means the business case can't be made. ⁓ as part building that business case, as part of going through that process of really understanding in detail what is data quality doing to us on a daily basis, build the knowledge to get to the bottom of what's gonna solve it.
Tom: Yep.
Jon: because there's actually only one chance to get it right, really, which is the point at which you put it in the system. ⁓ speaks to process, it speaks to compliance out on the front line of the business and ⁓ of bringing those people along with you. So it kind of the entry point really ⁓ Python code and language models. The entry point is just people and business. ⁓
Tom: No.
Jon: Again, that's sometimes a challenging realisation for explicitly technology-focused leaders and executives.
Tom: Yeah, yeah. It was really good. Everyone was kind of talking about it. It's not kind of segregated that you've kind of got the engineers in the operational side and kind of a back of house performance and data team. the thinking and the discussions were ⁓ integrated, like gets it and is there chipping away at it.
Jon: Yeah, yeah, yeah, ⁓ and there was focus and there was a realisation that it was, it not going away. ⁓ And it was something that ⁓ need to keep going with. and, we genuinely very impressed with not just with the mature clients, but of the stories and the interactions with ⁓ the less mature clients were even more impressive because the efforts kind of move those organizations forward. ⁓ kind the hours around doing other day jobs kind of thing, you know, was ⁓ really impressive the of the issues that some of those individuals were, ⁓ were seriously grappling with. people ⁓ get meet them and
Tom: Yeah. Yeah. Yeah. Yeah, it was really good. Yeah, it was amazing.
Jon: And it was really good.
Tom: Yeah, yeah, yeah. I think to some reason, it's kind of ⁓ all maturity that spoke to ⁓ are kind of challenge, but they're kind of working through it. how do we just keep kind of chipping away ⁓ at better and better quality, and better link from data through to decision making, and that kind of increased realisation that technology is an operational cost, ⁓ but that kind of brings it to forefront of the business. ⁓
Jon: Yeah. Yeah, yeah.
Tom: favorite meal.
Jon: on the beach at Manly.
Tom: That was really good, wasn't it?
Jon: In part two, we're gonna delve a little bit deeper into, systems, applications, platforms and data and what's the difference between all of those things and how do we solve all of them together? Do we need to solve all of them together?
Tom: exciting.
Tom: Welcome back to Transform. joining us for part one of a three part conversation John and I's ⁓ journey across Australia.
Tash Wilson: You've been listening to Transform by GHD. Big ideas, real impact. Welcome to Transform, tomorrow's thinking today. Real conversations on the innovation shaping businesses and our society.
Tom: I'm Tom Biederman, business group leader for Data and Insights at GHD, ⁓ joined by John Jarrett, leader for Data Insights at GHD. ⁓ What were your key take homes that discussion?
Jon: I'm not sure one specific right? But it was ⁓ amazing to talk to so many clients. And I think the ⁓ one thing that did stand out more anything else was the range of maturities ⁓ across the data information and technology space that base in line with what you would probably expect in terms of ⁓ different ways in which they're funded,
Tom: ⁓ so John, we GHD back in January 2020. And since that time, we've of built ⁓ a data advisory and team ⁓ in the UK. There's of around 40 of us ⁓ now working across a range of different infrastructure types. So some quite exciting projects, ⁓ the kind of scale the projects has been increasing. ⁓ Yeah. Obviously, GHD in the UK is quite small. There's about 350 people in total, of which we're beginning to be a kind of large-ish proportion. But then the question was kind Australia, obviously, GHD has got a larger presence. But the kind of data advisory, kind of direct to clients is quite a of a thin layer. So is there opportunity for us to be kind of serving clients better?
Jon: scopes of different business models, etc. So ⁓ we saw everything ⁓ privately funded clients in the private space around freight and ⁓ tolling that were more advanced than the cutting edge in infrastructure space here ⁓ in the UK. And ⁓ government funded with very and thin budgets in that space that were
Tom: Yep. Yeah. by ⁓ bolting on data services or providing data infrastructure services direct into clients. So a bit of a fact-finding mission really. And it was really good. had three weeks there. ⁓ It was really good to have those.
Jon: but still to with ⁓ the same of fundamental issues ⁓ as better clients. So really impressive work being across the piece, really, ⁓ that level of maturity
Tom: those discussions really good opportunity just to kind of, asking clients like, where are you with your kind of data, your systems for managing your infrastructure? ⁓ And also an opportunity for us to kind of bring over kind of you know, where is the UK industry at the moment? What's working? What's not working? What are ⁓ the priorities of different clients ⁓ and that kind of thing. What are the biggest challenges that we were seeing and what are they causing to those clients?
Jon: of resilience in the basics is an answer that essentially ⁓ out a number of different ways. And it fascinating to talk through the number of clients that were dealing with a lack of clarity in their data and real basics around does the data mean? What do we mean when we say a bridge, when we say a road, when we use individual terms, particularly clients that were trying to bring together multiple legacy systems that covered the same data domain, so multiple systems that covered property of organisational mergers, ⁓ Data was an issue that probably as always came up, ⁓ although interestingly saw both.
Tom: Yeah.
Jon: clients capabilities the marketplace that were really kind of much more focused on resolving data quality issues than we see here in the UK. ⁓ And some clients that have really gone the extra mile build the business case for data quality and to resolve it, which is really interesting and a ⁓ super interesting reflection for us kind of coming back to the UK.
Tom: Yeah, yeah, that definitely stood out. Yeah, kind of stands out how the kind of industry dynamics are very different actually UK compared to Australia, ⁓ mining as an example where you to keep your assets going. then therefore, it's worth investing in the systems, but the high quality data in the systems, ⁓ which you often don't see in the ⁓
Jon: and Yeah, absolutely. Yeah, resource extraction was kind of the sector that has really managed to make that connection between underlying data and end profit, which is, which is obviously very difficult for a publicly funded railway to do. ⁓ But seeing kind of next to each other, literally ⁓ in the next office in ⁓ in some
Tom: Yeah. No,
Jon: the difference in those business models and kind of the impact that that had on their ability to deploy data information technology into the asset management and the asset lifecycle space was really fascinating.
Tom: Yep. Yep. Yep. Would you say everyone had a kind of vision as to where they were going?
Jon: I think as ever with technology, people use it in their personal lives. People see how things are moving and particularly now we're ⁓ fairly into the AI revolution. People want that and they want to deploy that. I'm not sure it's ⁓ as coherent as a vision, ⁓ there's a very clear and well understood thread of we know we should be better, we want to be better, ⁓ we want to go there as a business. ⁓ think what was interesting was ⁓ that were really succeeding and really making kind of super impressive progress in that were the people that had connected that desire to be better.
Tom: Yeah, yeah.
Jon: to the underlying business model of their businesses and the organizations that were struggling and long-term about actually improving and deploying ⁓ were where that connection to the business model was understood to be weak.
Tom: Yep. And I think maybe a common theme across the UK and Australia was that kind of growing understanding, like technology is becoming an operational cost. So it's kind of an operational aspect of the business, isn't it? A lot of investment over the last kind of 10, 15, in some case, 20 years, but it's not static. And to keep it going, it's investing in the data of your infrastructure. And that kind of builds you on the journey towards more and more.
Jon: Yeah, absolutely. Yeah.
Tom: insight about how your assets are operating.
Jon: Yeah. we're struggling to understand whether ongoing investment in data quality, ⁓ is required, ⁓ understand just thermodynamically, we can't leave this stuff. Entropy is going to come and get us. ⁓ data is going to continue to degrade. It needs be up kept. But if we haven't correctly understood that that is OPEX and needs to be funded as OPEX and there's a key part of
Tom: Yeah.
Jon: delivering the service on a day-to-day basis and kind of achieving those day-to-day objectives. And we're still treating that piece as individual slices of capital investment. that's ⁓ a challenge because not capital investment, ongoing operational costs. ⁓ And getting that aligned, think, was clearly a challenge ⁓ some places. That's not say that the UK is brilliant at that, but I think there are certainly for of the infrastructure majors here ⁓ the UK, the with the regulator has enabled ⁓ it to be clear that that is OPEX. So it's not necessarily super strong connection ⁓ to the business case, you've got the presence of a regulator there that says, no, that's OPEX. And ⁓ we all accept that that's OPEX and we're on that basis.
Tom: Yeah, yeah, yeah, so there's a nuclear Yeah. Yeah, yeah. But even there, like in the UK, still finding its feet on how to address that, I would say. And if you roll back a few years, a few more years, like 2018 but ⁓ organisations be building their kind of like in-house ⁓ data, science teams as their kind of solution to that. But then you've got to kind of keep that going. ⁓ There's kind of talent management aspect to it and those kind of things. And then it's actually realising that
Jon: Yeah.
Tom: some of it is kind of more like moving as the systems and products are moving and making use of the tools as they're coming which is what you're paying your license costs for anyway.
Jon: Yeah. Yeah. it's immensely for an infrastructure organisation to be as agile as they need to be in that space, right? ⁓ a a big tech major, they will chop and change ⁓ the focus of their what they're doing, what they're investing in on an almost weekly basis when things are moving fast, ⁓ daily if required.
Tom: Yeah.
Jon: You can't do that in regulated safety critical business. can't taking an axe to the org structure ⁓ every week. ⁓ It be chaos. So you to balance that safety critical requirement stability and ⁓ allowing people to what is happening on a daily basis ⁓ with the need ⁓ refresh and continually ⁓ the systems piece, data integration piece, ⁓ your analytics piece.
Tom: infrastructure.
Jon: AI disrupts entire flow in some way, shape or form ⁓ in that ⁓ again a lot of thinking about and ⁓ a of careful planning to get them right, particularly if safety is involved.
Tom: yeah. ⁓ what would we kind of say as a simple set of organisations can be taking to kind of clean structure their data so their decisions can become ⁓ and more auditable, defensible and valuable.
Jon: I think the first thing is sure the business case is there, right? Like understand what is data quality doing on the front line of the business. And I think, a couple of clients out, naming their names, of course, but like a couple of clients stick out where they clearly understood that a visceral identity level, the poor data quality. was significantly hampering their ability to execute on a daily basis, but they could not attach a dollar sign to that. And that means the business case can't be made. ⁓ as part building that business case, as part of going through that process of really understanding in detail what is data quality doing to us on a daily basis, build the knowledge to get to the bottom of what's gonna solve it.
Tom: Yep.
Jon: because there's actually only one chance to get it right, really, which is the point at which you put it in the system. ⁓ speaks to process, it speaks to compliance out on the front line of the business and ⁓ of bringing those people along with you. So it kind of the entry point really ⁓ Python code and language models. The entry point is just people and business. ⁓
Tom: No.
Jon: Again, that's sometimes a challenging realisation for explicitly technology-focused leaders and executives.
Tom: Yeah, yeah. It was really good. Everyone was kind of talking about it. It's not kind of segregated that you've kind of got the engineers in the operational side and kind of a back of house performance and data team. the thinking and the discussions were ⁓ integrated, like gets it and is there chipping away at it.
Jon: Yeah, yeah, yeah, ⁓ and there was focus and there was a realisation that it was, it not going away. ⁓ And it was something that ⁓ need to keep going with. and, we genuinely very impressed with not just with the mature clients, but of the stories and the interactions with ⁓ the less mature clients were even more impressive because the efforts kind of move those organizations forward. ⁓ kind the hours around doing other day jobs kind of thing, you know, was ⁓ really impressive the of the issues that some of those individuals were, ⁓ were seriously grappling with. people ⁓ get meet them and
Tom: Yeah. Yeah. Yeah. Yeah, it was really good. Yeah, it was amazing.
Jon: And it was really good.
Tom: Yeah, yeah, yeah. I think to some reason, it's kind of ⁓ all maturity that spoke to ⁓ are kind of challenge, but they're kind of working through it. how do we just keep kind of chipping away ⁓ at better and better quality, and better link from data through to decision making, and that kind of increased realisation that technology is an operational cost, ⁓ but that kind of brings it to forefront of the business. ⁓
Jon: Yeah. Yeah, yeah.
Tom: favorite meal.
Jon: on the beach at Manly.
Tom: That was really good, wasn't it?
Jon: In part two, we're gonna delve a little bit deeper into, systems, applications, platforms and data and what's the difference between all of those things and how do we solve all of them together? Do we need to solve all of them together?
Tom: exciting.
