Data: The hidden engine in mining and its ripple effect
In mining, big gains often come from small shifts and nowhere is this more true than with data. While physical assets like rigs and machinery are highly visible drivers of productivity, the hidden engine of performance often lies in how data is managed and flows through your organisation. Often overlooked, effective data management is one of the most underrated drivers of productivity in mining and small improvements here can create big ripples across your entire operation.
In the latest episode of the acQuire Connected podcast, Steve Mundell, Chief Technical Officer, and Stuart van de Water, Sustainability Portfolio Leader at acQuire, unpack why data management is one of the most overlooked, yet powerful, levers for driving productivity. They explore how even small shifts in how data is captured, shared, and used can create a ripple effect across safety, compliance, decision-making, and effect stress levels on site.
Listen to the full episode here or read on for the key takeaways from the episode.
Why is data management a hidden productivity booster?
In mining, data touches every corner of the operation, from deciding where to drill, to monitoring environmental compliance, equipment health, safety metrics, and investment decisions.
“Productivity in data management is really all about reducing the time and effort between the capture of the data and then making a decision,” shares Steve Mundell
Yet data flows are rarely top of mind when productivity is evaluated. Steve explains that early-stage decisions at the drilling stage can have cascading effects throughout the operation, affecting scheduling and safety through to financial investments.
Stuart adds, “Even small changes in how data is collected and shared can have a huge impact on day-to-day operations”.
For geologists, faster model updates lead to quicker, better decisions. Operators can get clear, accurate instructions sooner to reduce downtime. And for executives and stakeholders, it’s more timely and trustworthy reporting.
However, the biggest drag on productivity isn’t always the data itself, it’s how it flows. And often, the people and processes behind that flow introduce delays.
“The bottlenecks are the things that kill the flow of data… especially at scale these things become less productive. When data arrives too late or incomplete, it often loses much of its value, leading to missed opportunities and fragmented decision-making,” shares Stuart van de Water.
What role does organisational culture play in seeing data as an asset or a burden?
Effective data management doesn’t happen in isolation; it’s directly influenced by the culture of the organisation.
“If an organisational culture is founded on collaboration and transparency, that’s going to flow down into how they work with their data — encouraging sharing and reducing silos,” says Steve Mundell.
Conversely, a culture focused on short-term results, quick fixes, or isolated solutions can result in fragmented data practices, disjointed systems, siloed teams, and inconsistent information flow – all of which diminish the data’s value.
Steve Mundell says, “You can see how an organisational culture can trickle down into how people view data and whether they see it as a burden or an asset”.
In short, organisations that embrace system-level thinking and openness are better positioned to turn data into a strategic advantage.
What is the ripple effect of weak data management down the mining operation?
Poor data practices don’t just impact isolated tasks; they ripple across the entire mining operation.
“I’ll get my data, notice some issues, fix it a bit, then throw it over the fence…not my issue anymore. But the ripple effect grows louder and louder the farther it goes down the chain,” shares Stuart van de Water.
Weak data flows cause costly delays and add unnecessary stress to teams responsible for analysis, reporting, and compliance. In environmental departments, for example, inaccurate or late data can create significant pressure and even lead to sleep loss among staff.
“The sheer amount of stress that person who’s now responsible for providing that [environmental data] is actually quite an impactful thing… we often have people say they’re literally losing sleep.” shares Stuart van de Water.
The impact goes beyond the toll on people. From a geological perspective, delayed or flawed data affects cost decisions, safety assessments, and scheduling with downstream consequences for multiple teams.
“Right at the start, data collected during drilling can determine whether you keep on drilling or not. That has an immediate cost and scheduling impact… it can also be used in models which underpin estimations on which investments are made,” shares Steve Mundell.
Stuart likens the situation to a traffic system:
“Think of a road or rail network. Sometimes the flow is fine out wide, but as things get closer and denser, a small issue can quickly turn into a major bottleneck. That’s the multiplier effect of poor data management.”
Rather than trying to fix everything at once, Stuart suggests a strategic focus, “It’s about identifying the changes that will have a tangible, measurable impact – the ones that ripple outward and multiply.”
Listen to the full episode here:
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Read the full transcript here:
Intro/Outro (00:00): Welcome to acQuire Connected, the podcast that is your compass in the world of data across environmental, social, and governance.
Jaimee Nobbs (00:10): Welcome back to the fourth season of the acQuire Connected podcast, where we chat to thought leaders and tech experts who are tackling their data management challenges. I’m Jaimee Nobbs, your host for this podcast, and today we’ll be diving into one of our most powerful yet often underestimated drivers of productivity in mining operations: data management. We’re joined today by Steve Mundell, Chief Technical Officer at acQuire, and Stuart van de Water, Sustainability Portfolio Leader at acQuire, who will also be exploring the topic we’re discussing today at this year’s International Mining and Resources Conference (IMARC).
Now, when we talk about productivity in mining, a lot of the time optimising machine throughput, enhancing workforce efficiency, streamlining supply chains and improving safety are where our mind goes. But there’s another layer to all of this, and that’s the role of data, how we manage it and how effectively we use it can have ripple effects across safety, cost, reliability, and making strategic decisions. So today we’re asking what does productivity and data management really look like? Who does it impact and how can small improvements in how we handle data create massive returns across an operation? There’s a lot to cover. So let’s get into it.
Thank you for joining this episode today. You’ve both been on the podcast before, but for those who might not have listened to your previous episodes, are you able to start by introducing a bit about yourselves? Steve, do you want to start?
Steve Mundell (01:39): Sure thing. Yeah, I’ll kick it off. Yeah. So I’m Steve Mandell, I’m the CTO for acQuire. I head up the R&D function across acQuire.
Jaimee Nobbs (01:50): How about yourself, Stu?
Stuart van de Water (01:52): Yeah, I’m Stu van de Water and head up our Sustainability Portfolio of software products. So I’ve been with acQuire for about five years. I’ve been in software all my life or working life, started as a developer and now head up the sustainability portfolio and we look at those sustainability-focused software right across utilities, resource organisations and governments at various levels.
Jaimee Nobbs (02:13): Great, that’s exciting. There’s two new job titles there that I haven’t heard before on the podcast.
Stuart van de Water (02:18): First time I’ve said it out loud. It’s exciting.
Jaimee Nobbs (02:22): So today we’re exploring a topic that Stu, you’ll be diving much deeper into at this year’s IMARC, the International Mining and Resources Conference happening in Sydney this month, and that is around productivity in mining operations. So when we think of productivity and optimising mine operations, we usually look towards things like the throughput of machines, workforce productivity, supply chain logistics, improved safety processes when we consider productivity. When looking at managing data though, what does that look like and who does it actually impact?
Steve Mundell (02:59): So when looking at this one here, I think if we just look straight at the definition, productivity in data management is really all about reducing the time and effort between the capture of the data and then making a decision. So technically that’s the sort of things, but what does that actually look like in real life so we can be managing this data well in a productive fashion? But actually the business context of this is, what does it actually look like? So for geologists, it might be around faster model updates, so it can get the information that might be collected through drilling and then making it through into models, or been available to use in models through a much faster fashion. It’s operators getting clearer instructions faster, so reduced downtime, but also extending out to executives and other stakeholders. So, being able to get a more timely, trusted reporting or having confidence in your compliance and having that in a more timely fashion, I guess, is the key thing. But also, when we think about a timely fashion, it’s in the timeframes where that other function can actually make use of it, rather than just being the data management is making the data available faster, but it’s just sitting there doing nothing for a long time until it can be picked up. When we think about productivity, we’ve got to look at the entire picture, not just in one function.
Stuart van de Water (04:26): Yeah, I think that’s a really good point, Steve. In terms of looking at the entire picture, it’s really that lifecycle and all the interconnections, and for me it’s about identifying those bottlenecks. The bottlenecks are the things that sort of kill that flow of data and often the bottlenecks involve the people part of the process. So, it’s really trying to work out how data gets exchanged, moves in and out of the organisation and gets transformed. Looking for places where, especially at scale that these things become less productive. And like Steve said, these are often the times where you do have those delays in getting the right data to the right people in a timely fashion. So often when it’s late it almost loses a lot of its value. So that’s where the productivity for me comes in. It does really impact a lot of people across the business and sometimes it’s not quite known how it impacts people, which is even more dangerous.
Steve Mundell (05:14): Yeah, a key point is: what drives productivity is decisions being made. So, if a decision isn’t able to be made, then there’s no real progress and people are sitting around or equipment sitting around idle and waiting for the next thing to do. When we think about productivity and how data impacts that, it’s about having a focus on enabling decisions to be made.
Jaimee Nobbs (05:42): I guess looking at that flow of data from that point of capture right through to decision making and making sure that there’s no bottlenecks and that flow of data is as seamless as possible. If data doesn’t get to the right people in the right timeframe, then decisions can’t be made, which has a flow-on effect. In what ways do you see data management as a bit of a multiplier? So how would something like a small improvement in your data practice ripple out to affect safety, reliability, strategy or decisions down the line in a mining operation?
Stuart van de Water (06:16): Yeah, I think that’s a really good question because it actually is underpinned by the decision of which part you’re going to try to fix and the impact of that. So we can’t fix or try to change everything at once. It’s really about trying to identify those things which will have that tangible, measurable impact and you know that it’ll be able to have that ripple effect and multiply and we’ll be able to do things at scale. And I think a lot of the people we deal with, do work at pretty large scale. So you do have the opportunities to identify these things and say, look, we’re actually going to target this part because that will have a really good impact here, there and everywhere else. For me, it’s like thinking of a road or a rail network. Sometimes the further you’re out the flow is quite good, but once you start getting a bit closer in and things are more dense, then those sort of speed of decisions or speed of the ability to cope with that sort of traffic is pretty impactful when things go wrong. And you sort of see that multiplier in the ripple effect of one minor thing happening and then everything else getting affected.
So for me, it’s looking at where the best bang for buck is in terms of being able to apply that so you can have that sort of win-win situation as much as you can with, as Steve mentioned right at the start, all those different stakeholders and all those really timely decisions because not all decisions are equal, and so you do want to try to focus on the things which will have the most tangible benefit for everybody involved.
Steve Mundell (07:38): Yeah, I can give, I guess my thoughts there from a geological background, which is my pedigree, and think about information from a drill hole and where it gets used throughout a mining operation. Right at the start, as it’s been drilled, the information that’s being collected can help determine do you keep on drilling or not drilling. There’s an immediate cost impact as well as a scheduling impact of when they can move on to do the next thing. Then that information once collected, it can be used in a variety of different areas. It might be used in the geotech world for determining the stability or risk assessment around the stability of operations and whether it’s safe to enter areas. So, there’s a connection there to safety, but then that data can also be used in models which then underpin the estimations on which investments are made into expansions or whatever it might be. So, getting this information right upfront and rapidly, but then also working on those interfaces into other areas so that information can transfer to other parts of organisations rapidly, just shows how data can be used and improve performance and productivity.
Jaimee Nobbs (08:58): You’ve touched on a few different aspects of a mining operation that data management can really ripple out and one small decision how that can impact further down the line. What are the biggest risks when data management is weak then? So, you’ve mentioned planning, but for things like how does it impact a community or environmental compliance targets, how do weak data management practices impact there?
Stuart van de Water (09:25): That’s a good one. When we think about what weak data management practices mean, so I think a lot of people are quite good at managing data when things go well, but often things don’t go well. So thinking back to the reference to the road network, everything’s flowing along well and there’s one minor bingle and next minute everything’s backed up. So I guess part of data management for me is, what happens when things don’t go as well as you need them to? If data isn’t provided when it needs to, so going back to say that community reporting or environmental reporting, for example, having timely data is super important. They often have to submit to regulators and to other third parties and stakeholders, which is vitally important. But if they get to the end of the month and they realise they don’t have the data they need because something happened and the data didn’t come through, they weren’t informed in time, and then suddenly they’ve got to have this enormous effort to try to go back and find it, if it’s even there, dealing with multiple different people.
So, you think about that ripple effect of all the different people now who have gone the other way to go, now we have to almost try to work out where this is at. In some cases you’ll see then that people go, oh, we can’t even get it, so we’re going to have to use provisional or average data from another time, which isn’t even actual data. Suddenly you’re reporting and making decisions on data that may or may not even be relevant for that period. It definitely has a really large impact and there’s a lot of risks there when it is weak. So like I said, it’s just that ability to think about all those different things that can go wrong and how we mitigate against that.
Steve Mundell (10:56): You’re right there with weak data management. It can show up in a number of different ways, whether it’s availability, data, the quality of it, the security around it, so you can delve into that very deep and find all of the risks and how that might be mitigated. But I guess this one aspect of with data management, is it creates this decision lag which Stu alluded to there. So when there’s a problem out there, you don’t know until it’s too late. That can be quite a big cost, whether it’s financial or reputational, it can be multiplied out to a massive amount.
Stuart van de Water (11:37): And we’re sort of, focusing on the tangible outcomes of what weak data management impacts on, but actually don’t forget the human toll. It actually puts people in some really awkward and challenging positions. It’s massively stressful for people who have to have managed a lot of this stuff from day to day. So we say in the environmental space, where I’m most familiar, the enviro’s just not having the data available to them or having data that’s incorrect or not getting provided in certain times – the sheer amount of stress that person who’s now responsible for providing that is actually quite an impactful thing. And we often have people say, look, they lose a lot of sleep, literally are losing sleep, because these things aren’t organised. They’re not coming in on-time and it’s just causing a massive issue. So yes, absolutely big impacts to the business in order to be able to make decisions and report to the stakeholders. But the human toll of what’s happening inside the organisations is actually quite impactful too.
Jaimee Nobbs (12:33): A lot of different costs involved. So like you said, you’ve got the financial costs, but the cost of making decisions and how an operation runs and then how people are impacted as well when they can’t do their role and they can’t make their decisions as well, which impacts them. If we look at something you touched on earlier, which was that sometimes people can be bottlenecks, how does the culture of a mining organisation influence its approach to data? I’m interested to hear Stu, maybe if you want to start, if you look at, like you said, the toll it can take on people. Where do you sit with this?
Stuart van de Water (13:14): Yeah, I think that’s a bit twofold with this one. There’s definitely the human side, but there’s actually the entire organisation’s cultural philosophy around how they manage data. So in terms of the people side, if not everyone’s on board, you’ve got people in the enviro space and geology space out in the field with varying levels of technology savviness. They may or may not be entering things in, they might not embrace the technology. They might be still submitting things on paper with all of those sorts of issues. As an organisation, they might not be that stringent about the tools that they’re using. So there are a lot of Excel spreadsheets and there are a lot of other things that are going on, which are circumventing the ways that we’re meant to be doing things. So every one of those things can compromise the quality and the accuracy of the data timeliness of it, which as we’ve discussed it, it really does affect that productivity of using it.
But even right up to the organisational level, you get some organisations that go, we are just going to use a massive consolidated data platform that can manage any type of data. One size fits all. We just jam it in, and surely that’s enough, which is great. We see that because people think it might be cheaper, it’s going to be less vendors, there’s a lot less moving parts, we can get all the data in there, but it really doesn’t manage the individual expertise of what we need to do. So then you find companies that go, we just want best-of-breed and we’ll have an integration philosophy as well that brings it all together, but we need to ensure that the people have the right tools in the business to perform their jobs. So we do see that and see very large organisations and how they manage data. So I think probably the answers inbetween there somewhere, but that sort of culture or attitude towards how the company manages data can have a massive on productivity and trust of that data as well.
Steve Mundell (14:55): Yeah. Doubling down on or summarising some points that you made there. I guess seeing there that the culture of the organisation can guide or define whether the data is seen as a burden or an asset. So I guess if it’s an organisational culture that’s founded or has philosophies around collaboration and transparency, then obviously that’s going to flow down into how they work with their data and encourage sharing and reducing silos, which of course is one of those bottleneck kind of areas. If you’ve got a organisational culture around short-term fixes and just getting it done to the next day, it could lead to a fragmented or a point-solution type of approach to things as opposed to a systems approach. So you can see how an organisational culture that has philosophies can trickle down into how people would view the data and how they might work with it and see it as a burden or an asset.
Stuart van de Water (15:57): Absolutely, and even if they have a culture of lack of responsibility or accountability, then “I’ll get my data, ah there’s a few issues, I’ll just fix it up a little bit. That’ll be someone else’s problem. Throw it over the fence, not my issue anymore. Off I go.” Then the ripple effect, as we talked about before, just gets further and further and louder and louder, the farther it goes down the chain.
Steve Mundell (16:17): Yep, that’s it.
Jaimee Nobbs (16:19): So how much of data management in productivity is about opportunity versus constraints? So data is a burden or data as an asset. So what usually is holding a mind back from these best practices? Is it a lack of tools or software, lack of capability, organisational inertia, they’re like, it works, therefore why would I try and improve it? Or is it cost? Where do you see the biggest barriers?
Steve Mundell (16:46): Yeah, I guess in our experience, looking at how organisations that have done it well is there’s a big opportunity where organisations look at the system as a whole as opposed to individual silos or point solutions. So earlier on we were talking about you can improve the productivity in one area, but unless you’re looking at the productivity of the entire system, then it’s not really going to have the desired outcomes. So there’s certainly an opportunity there to look at the organisation or the system as a whole. There are certainly constraints there. You can work around in terms of legacy tools that don’t work well together or legacy processes that don’t work well. But I guess then there’s the opportunity of finding tools or utilising tools that do work. So digitalisation and those sort of initiatives really speak to the opportunities that are there for improving productivity through good data management.
Stuart van de Water (17:50): Yeah, we also see, I guess maybe call it lack of commitment as well, when companies with, we find that generally have the best of intentions and really trying to embrace data and data management practices, and so they will commit to purchasing a bit of software, putting it in, but then they don’t actually commit the time and the resources to do it properly. So we find, especially with the environmental side, that the people involved, they’re often very busy with a lot of field work, with a lot of reporting, with a lot of compliance work, and then there’s this thing, by the way, which we need to do to implement something which I may or may not believe in. So you get people who are often not available, who don’t make themselves available. So it’s really having that commitment for people involved, but also the stakeholders to ensure this thing actually goes through.
And I guess that’s also considering what the end state looks like. So people don’t often think, oh, once we’ve got this in, what does it look like? So we’ve actually had customers before that have mandated that the only reporting they’ll accept is out of, say, a system that we have, or it can’t come from Excel, it can’t come from anywhere else. You need to be able to show that it’s come out of one of our business systems, which helps really straighten up the thinking from day one to say, we have to do this, otherwise we’re not even going to meet our own KPIs. So there is that commitment to time. I think that idea of organisational inertia is really important, especially with the mining organisation. It will roll on anything you do that might affect production or some sort of productivity in that area is not generally supported. So it’s got to be about how people can do these things and implement it without a huge amount of disruption. But also if it’s deemed to be important, then how do we get that backing and that change management to support it?
Jaimee Nobbs (19:29): From a lot of your answers, I’m seeing that the organisational culture and the standards and the processes that an organisation sets up as a whole is really important, and then the element of accountability seems to be flowing through. If people aren’t responsible for what they’re putting in, it can have a flow on effect, but it is really important for the individual or the individual teams, for example, to be taking accountability for what goes in and that can impact the whole way through.
Stuart van de Water (19:59): Definitely. Sorry, Jaimee, it definitely can. And to pick up on that, it’s not just when it goes in, but when it’s there. So the actual prolonged usage of these things sometimes wanes, so everyone gets excited, they put it in. Then people, if they don’t have the change management to change the way that they do things, go back to old ways. They stop utilising the products that are there and you actually get into this large mess reasonably quickly to say, look, there’s all this stuff going in there. No one’s been proactively monitoring it. So again, that human element in this process is super important to ensure that these things not only succeed in the first part, but also then thrive through that and get the actual benefits that the company intended to get in the first place.
Jaimee Nobbs (20:38): Yeah, it’s definitely what we’ve spoken about before, the people, the process, the technology, having all three in place to be able to successfully make those decisions. I have one last question for you both over time. How does improving data management contribute to the resilience of the company? So for example, adapting to commodity price swings or regulatory changes. We see, for example, in the environmental space there’s a lot of change, a lot more stringency around emissions targets and those sorts of things and community and environmental pressures.
Steve Mundell (21:13): So with resilience, yeah, looking at that. So I guess being able to adapt to change. Well, so if we think about good data management practises are improved data management, it’s seeing your data as an asset and managing that way so that you can rely on it now and into the future so that the next generation of employees in the organisation can use that information. So you duon’t know what you don’t know, so you don’t know how it’s going to be used. So, if you’ve got good data management in place, then you do have an asset that you can rely and go back on and scenario plan in the future. You don’t have to scramble around trying to understand that data and wrangle with it to get in into a form that you can use. It’s there, available, ready to go. So, I guess resilience, from my perspective, comes from seeing the data as an asset and treating it so.
Stuart van de Water (22:08): And getting it to an asset will take a bit of time. So, people often have to know that we’ve actually got those processes in place. You’ve probably gone through a few rounds of auditing and going, yeah, actually what we’ve set up is actually really good and bulletproof. Also that you’re guarding against complacency because often once you’ve got everything up and running, then people get complacent and things can fall off. So, once you’ve got those things in place, like Steve said, you actually get to that point where you’re going, yeah, we can trust this data. We don’t have to crosscheck it. Second guess it, revalidate it, all of those things, which hamstrings a lot of organisations to be able to make those decisions, especially at pace. Something comes in, they’re not second- guessing what’s sitting behind it. They’re going, cool, I know I can grab this from here and grab that from there because we’ve thought about it and architected it nicely. I’ve got it at my fingertips. I don’t have to go ask that person to ask that person or submit a ticket that takes a day. I can just get straight into it, which I think that’s a really good place to be and it does take time and it does take a lot of dedication to get there.
Jaimee Nobbs (23:05): Yeah, I think particularly maybe for future modelling, not having to, like you said Steve, wrangle with that data and also what you said before, trying to almost guesstimate based on historical data, for example. Having that data there, having it ready to go is so important.
Steve Mundell (23:24): Another way that can contribute to resilience is being able to predict or to anticipate change rather than just react. So, to Stu’s point of putting in effort over time, organisations will be on a journey, so to speak, and everyone’s going to be on a different place in that journey from that starting point of having the barest minimum to improving how they do things so that they can get to where they want to get to it. Different organisations will want to get to different levels of complexity or sophistication, but some organisations will be happy to just be able to react, but react well. To be able to know that there is some information there they can rely on to work out this situation. Whereas other organisations, I see it in their interest to be able to simulate or to anticipate different scenarios and how they’re going to deal with those in the future if they come up. So improving data management practices in this sort of area needs to be aligned with where the organisation wishes to get to so that it’s something that they can put all of their effort into achieving and get there rather than trying to be someone who they’re not.
Stuart van de Water (24:39): It’s a good one, Steve. I’ve actually probably got a couple of more points on that as well. In recent times, I’ve sat in on a few things where even when it comes down to compliance reporting, it’s not often the answer; it’s about showing your workings and actually showing that you’ve got these things in place which contribute to a result. So, the absence of that is actually sometimes the things that the regulators will be looking for. So not having thought about this, like Steve said, you don’t have to have everything in place. It’s just that aspiration or at least plan to get somewhere, which is the first step, and then implementing those things is super important as well. So then people will be far happier to work with you in terms of those sort of things, especially when it comes down to regulatory reporting. But coming back to that question of resilience, I think a really important part coming back to the people again, is that the last thing you want to happen is saying, “Oh, we need to make a decision. Steve’s on leave. Oh, okay, we’ll wait till Steve gets back and then we can ask him about his database or his data and off we go.” For me, that’s one of the things that we do see a fair bit of, especially before we implement some of our software, and that’s where that resilience comes in. So, it’s not relying on people having these little silos of information or in their head, but you can unlock a lot of those things and make sure that it is implemented throughout the chain. So, we’ve talked about it’s available, it’s there, we know how it integrates with other things, and it’s in a usable state, which we can utilise.
Jaimee Nobbs (25:56): That’s a really good point. The resilience really does come down to what a company needs and what resilience within a company actually looks like for them. Because a large mining operation with multiple sites across multiple jurisdictions will look very different to a much smaller exploration company or even local council for some of the environmental regulatory reporting as well. So it does really come down to what does resilience actually mean and look like for each individual company.
Stuart van de Water (26:29): Yeah, we’ve got an implementation now where we actually can’t do anything for a week or two because the person’s on leave and that’s the only person that knows about this particular bit of data.
Jaimee Nobbs (26:39): The human equivalent of the black box.
Stuart van de Water (26:42): Pretty much.
Jaimee Nobbs (26:44): I think that’s a really great spot to leave this episode. Like I said earlier, Stu, you’ll be speaking at IMARC, so if people want to hear more, you’ll be diving into a little bit about this topic, so I can link that in the show notes if people want to go check out where you are and what time you’re speaking. But thank you both so much for joining this episode. I really enjoyed it.
Stuart van de Water (27:08): Thanks, Jaimee.
Steve Mundell (27:09): Good to be part of it. Thanks, Jaimee.
Jaimee Nobbs (27:12): Thanks for listening to this episode of acQuire Connected. If you enjoyed today’s episode, please like, share and subscribe on your podcast player. We’ll see you next week for another episode.
Intro/Outro (27:22): Thanks for listening to the acQuire Connected podcast channel. Find us@acQuire.com au.
