acQuire Connected episode 40: Turning complex data challenges into critical business intelligence

Making informed decisions is more crucial than ever for the mining and resources sector. Whether it’s monitoring environmental impact, ensuring regulatory compliance, or optimising resource extraction, businesses rely on vast amounts of data to drive efficiency and sustainability. But data alone is not enough. Companies need the right people, processes and technology in place to transform data into actionable insights as the key to success. 

To explore how industry leaders are using technology to tackle their complex data challenges, acQuire hosted a panel discussion at PDAC 2025, led by acQuire CEO Alison Atkins, featuring guest speakers: 

• Kyra Meyer, Managing Director at Kyfer Operational Improvement  

• Cornelia Holtzhausen, Executive Management in mining, and  

• David Henderson, Chief Growth Officer of the Americas at Fleet Space Technologies.  

Listen to the full episode here:

Or read on for our top three takeaways from the conversation. 

Takeaway 1: Effective decision-making starts with a strong data foundation

The way geological data is captured has changed significantly over the past few decades – gone are the days of logging on paper, using crosscuts and saving data to floppy disks.  

To manage the volume and variety of data collected across the business, companies must first invest in their data integrity. That means implementing the right systems and processes and upskilling teams to convert raw data into meaningful insights. 

It starts with understanding why you’re collecting the data. As Kyra Meyer explains: “That first step is going to streamline the process dramatically and actually get people more engaged in the process of capturing that information in a more meaningful way.” 

David Henderson also highlights the risks of fragmented data: “You’ll have geophysical data, geological data, maybe your core photos are stored in one place, and they’re all in different mines. Siloed data and data coming from different mine sites or in different formats risks a problem, especially when you’re trying to bring in these data sets quickly and efficiently.” 

The solution? “Make sure you have clean data in a centralised place where you can utilise it over and over again”, said David. 

Cornelia Holtzhausen then emphasises the value of data visibility on decision-making at the operational level, “The moment an operator can have visibility on the data, it allows the operator as well to make better decisions. We need to be able to translate that into our operational plans and into the future, to help us extract the value from resources to achieve their optimum potential.” 

Takeaway 2: Real-time access to more data drives faster and more confident decision-making on the ground 

Access to real-time or near-real-time data gives operational teams the ability to respond quickly and effectively to changing conditions so they can make faster decisions to improve performance and reduce delays. 

“It’s the velocity to make decisions… If I’m sitting in a seat as a supervisor of an underground drill rig, and I want to know how it’s going, what are the results looking like, what are we getting out of the ground… if I can get access to real-time information or as close as possible, then I’m winning as a professional,” shares Kyra Meyer. 

David Henderson pointed to the shift in technology that makes this possible, “Sensors aren’t expensive anymore… storing data isn’t expensive anymore… we’re in a space where there’s a lot more data… getting it to the user right away is how we’re going to see the industry move forward.” 

But more data also demands better structure behind the scenes. As Kyra noted, “Having more data available right now is making our lives easier. It’s giving us more possibilities of thinking outside the box and defining new ways of doing things. But we always need standards and processes behind the scenes, even though those two words are very boring, but they’re absolutely necessary.” 

Takeaway 3: Strong leadership and role clarity will help unlock the value of data 

Clear leadership and well-defined roles are essential for teams to confidently interpret and use data to achieve operational goals. 

Cornelia Holtzhausen outlined the responsibilities of a leader, sharing, “We have a lot of data, but to convert that into meaningful information is the responsibility of leaders to not to get entangled in the data, but actually being the ones that ask questions and help to feed it.” 

Kyra Meyer then links the importance of role definition to each individual’s purpose, “When we look at how do we really make decisions, it’s about purpose. For me, to be able to understand what I need to do, my role has a purpose. And with the purpose of my role, I have goals, and I have targets. And that’s the same for anybody in a team. If I understand my goals, and I understand what I’m trying to achieve, then I’m going to want the data that allows me to be able to do that.”  

With that clarity, data becomes a tool to support decision-making rather than a barrier. 

When individuals understand their purpose and objectives, data can become for confident decision-making, not a barrier. And as the industry evolves, leaders who can support this shift and clearly define expectations will be best placed to turn data into action. 

To listen to more episodes like this, head to our podcast player here or listen along on Spotify or Apple Podcasts. 

Read the full transcript below: 

Intro (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): Hello, I’m Jaimee Nobbs and you’re listening to the acQuire Connected podcast. If you’ve listened to our episodes before, welcome back. And if this is your first time, also welcome. On this podcast, we chat with thought leaders and tech experts who are tackling data management challenges across the geoscientific, environmental and social performance industries, and their impacts on the earth’s resources, the natural environment and communities around them.

Today, we are featuring the panel discussion from the tradeshow floor at this year’s Prospectors and Developers Association of Canada, also known as PDAC. The theme for this panel discussion is, “From Data to Decisions: Turning Complex Data Challenges into Critical Business Intelligence. This discussion was facilitated by Alison Atkins, CEO at acQuire, and Alison is joined by three industry speakers, David Henderson, Chief Growth Officer of the Americas at Fleet Space Technologies, Cornelia Holtzhausen, Mining Executive and Kyra Meyer, Managing Director at Kyfer Operational Improvement. Now that’s enough from me. Let’s get into the discussion.

Alison Atkins (1:15): Thanks everyone for attending our annual PDAC panel discussion. This year’s theme, “From Data to Decisions: Turning Complex Data Challenges into Critical Business Intelligence”. So I guess it’s something we’re quite passionate about at acQuire. I mean, we are very much an information management house. We have multiple systems and it’s all about really the integrity and the quality of your data and the compliance of that data. And today I’m actually quite happy to introduce three people who have volunteered their time to assist with the conversation. And I just wanted to kick things off by introducing the three panelists. Starting from the end, we have Kyra Meyer.

Now, Kyra is a systems and innovation leader in the mining industry who brings value to organisations using performance frameworks and operational technology solutions. Her current focus is helping companies increase their efficiencies, reduce costs, and improve their safety indicators through implementing management operating systems, strategies and roadmaps. It’s a pretty impressive description there, Kyra. So, Kyra is the CEO and Managing Director for a consulting boutique, kyfer.

Next to Kyra, we have Dave between the two ladies. So, Dave has a strong background and a long association with acQuire coming from Calgary as well where we have a presence. He has a strong background in scaling technology businesses and optimising data-driven exploration through his previous leadership roles at tech companies operating in mining and oil and gas, leveraging his expertise in AI, robotics and SaaS to drive innovation in mining. He’s currently the Chief Growth Officer for North America or the Americas for Fleet Space Technologies.

And lastly, we have Cornelia Holzhausen. Now Cornelia is a seasoned executive leader who drives transformation and sustainable growth in mining across global markets. With her industry expertise, she delivers results, fosters high performing teams and aligns business goals with community and environmental priorities. So again, when you look at the suite of technologies we have, it very much compliments what we’re doing here at acQuire. Cornelia has led profitability, efficiency and strategic transitions for a variety of organisations. So, thank you all for participating in today’s conversation.

What we’ll be doing is we’ll be asking a few questions from the panelists, and at the end of that time, we will open the floor for a few questions from the audience. And then proceeding the panel, we will be offering refreshments and the opportunity to network with each other as well. So, I’m going to start off the questions and I’ll start with Cornelia this time. I’ll start this way. So just with the first question, and this is all around volume and complexity of data. How do you see that it’s impacted your current or maybe previous roles, because having worked in the industry for a while, and the way that you make decisions from an operational activity perspective or from a mining perspective?

Cornelia Holzhausen (4:22): So, I just want to remind everyone where we started when I started, so I’m going to give my age away here. So, when I started, geology was on a sheet of paper where you have crosscuts and you had a huge room for the lab equipment with these big floppies. Data have changed significantly, and I think we just need to give ourselves recognition for what we’ve obtained in the mining industry. With that change in data comes a significant accountability as a leader because you need to set up clever dashboards for people to allow them to extract the data into meaningful decisions.

There’s also a requirement to ensure that you have data integrity. It requires us to ensure our people are trained and able to actually be data savvy, and to understand that you can now use data across multiple functions. So, it’s more than just ensuring your information is correct, but also how can you utilise the information across different functions. The other important role that you have to place to be in the forefront, ensuring that you have the appropriate cybersecurity and taking people along because there’s a lot of resistance in terms of data and the concerns people have, and how do we build communities where our people and our teams are part of the solutions that we can extract from data.

Alison Atkins (6:00): Absolutely. And again, showing my age, being a geologist quite a while ago, logging for me was very much on paper and pen and the big AO plotters and yes, so completely understand where you’re coming from. We’re still very young by the way. I’m going to hand over to Dave.

David Henderson (6:18): Hi. Thanks Alison. Yeah. So, throughout my career we’ve leveraged data, we’ve leveraged technology, and now everybody hears the big thing, AI. AI is everywhere. So what I’ve noticed in the mining space is, and you’ll love this answer Alison, is we don’t have enough data. Our CEO is speaking today at Fleet Space. At Fleet Space we’re bringing in data from satellites, we’re bringing in data from truth data from ground data, from coring data. And the stat I heard this morning is that 15 terabytes of data is the average per mine, where the average data per Tesla is about 150 terabytes. So, if you put that in perspective, we’re not really using that much data and the way that AI works is the more data the better it actually predicts. So, in my opinion, what we’re going to see or what we should be seeing is a lot more data.

And I mean just walking around the conference today, I saw there’s a lot of people looking for money out there. A lot of people are trying to raise money, and I remember a stat I read a couple of weeks ago and it was saying, of the major mining companies, they’re putting in of their total revenue, they’re spending about half a percent on research and development. So that’s actually pretty low. For research and development, I would say on average, companies should be spending like 3 – 8%. So, the mining industry is ready for a push in money, push in data, and I think highly organised data and usable data is where we’re going to see the future.

Kyra Meyer (7:54): Thank you. Okay, so I’m going to crash the party a little bit. So in this particular one, I was giving it a thought while I was having lunch yesterday with a friend of mine. And we were discussing, okay, so what is the big difference between data and information? Now, we’re sipping our soups and looking at each other and say we’re going to have this discussion again. It turns out that one flavour that I would love to add to the conversation here is we may have a lot of data, tonne of data and tonnes of pieces all over the place, but for us to be able to turn that into information and actually make decisions out of it, I would love to invite the crowd to think about why are we capturing the data and what is the purpose of that data, and what kind of decisions we want to make out of that data. Because they’re going to streamline the process dramatically and actually get people more engaged into the process dramatically and actually capturing that information in a more meaningful way. Going back to you Cornelia on that particular matter of people’s kind of resistance to that.

Alison Atkins (8:50): Oh look, absolutely. And I guess that’s where we’ve strategically looked at acQuire. I mean we are very much in information management house. There is collecting the data, but it’s firstly understanding why and what are you going to do with it? And then ensuring that it’s quality data to ensure you’re making the right decisions or confident decisions. So yeah, I love your distinction between the two, so thank you for that.

We’ll move on to the second question, and I’m going to get Dave to start first on this one. In situations where real-time data is critical for decision making, how can companies ensure the right information gets to the right people at the right time to address operational challenges?

David Henderson (9:35): Sure, yeah. So, what I’ve seen in my experience is a lot of the data is siloed and it’s not in one place. You’ll have geophysical data, you’ll have geological data, maybe your core photos are stored in one place and they’re all in different minds. What we’ve seen, especially at Fleet, the reason that Fleet Space connects to satellites is so that we can get data to the user quickly. So, a model might’ve taken, let’s say three months to a year to get going; connecting to a satellite, you’re getting that data within days. So, I think with data, the big thing is to make sure, I mean they always say it, garbage in, garbage out, to make sure you have clean data in a centralised place where you can utilise it over and over again. I think the siloed data and data at different mine sites or different formats really risks a problem, especially when you’re trying to bring in these data sets quickly and efficiently. I know I was the president of Geologic AI and we used to bring in all these other old data sets and with these older data sets, it used to take core scanning from weeks to months. So quicker, faster, more efficient, clean data helps us make decisions.

Alison Atkins (10:45): Absolutely, and I guess really when you start looking at silos as well, which silo has the real truth and the original observations and measurements as well.

David Henderson (10:55): And how do you trust it? I think the big thing is when you’re getting data and it’s not clean and you’re bringing it in, it goes to that old thing, garbage in, garbage out. Do you really trust the data? Is it useful? So I think that’s changing as well. People are putting in similar data sets, they trust it, and then when they go on to use it later on, it’s a usable set that you can leverage over and over again.

Alison Atkins (11:15): Absolutely, and I guess it really comes back to what you were saying earlier Cornelia, around adoption and trust of the people consuming the data or collecting the data as well. It is ensuring that it’s that quality of the information that they’re collecting.

Cornelia Holzhausen (11:30): And just on top of that it’s also to ensure that you can actually utilise the data across. Working currently with the GMG on a geometallurgical project where you have data from mining metallurgy geology. Finding a standard way to represent the data so that you can actually use it across functions and build the right models becomes a challenging exercise.

David Henderson (11:55): What I’m seeing is a big change. Before people used to use small data sets to train, and now with AI we’re using bigger data sets. So, you might have a crappy data set here and there, but at the end more data wins. So, the more data you have, the better you’re going to answer your problem in the end, I think.

Alison Atkins (12:18): Absolutely. I’m going to pass over to Kyra now. I know she’s chomping at the bit here.

Kyra Meyer (12:22): Yeah, no, I love this debate. I love hearing your opinions on that. I would say that in my opinion, when we look at how we can get the data to the right people to make the right decisions at the right time, we’re describing a process. And what we are trying to depict into that it’s some sort of management operating system. If you guys remember the plan, do, check, act will, then everybody has seen it before. So, when we look at it and we point that out into how to get the data to the right people to make the right decisions, it’s all about defining the processes, not only, but also the algorithms and the decision trees. And when I see these, what do I do? And when I do that, then what happens? So that actually streamlines the procedure for people to actually make decisions and release a little bit of the stress that some of the operators may have when they look at big data sets and says, okay, so now what? What do I do now? So having more data available right now is actually making our lives easier. It’s actually giving us more possibilities of thinking out of the box and defining new ways of doing things. But we always need standards and processes behind the scenes, even though those two words are very boring, but they’re absolutely necessary.

Alison Atkins (13:40): Oh, absolutely. And again, it comes back to GMD standards, particularly in our space. I mean we’ve been trying to adopt them for so long across all the different types of information we’re collecting, but I think it’s really coming to its fore now that organisations and mining companies are starting to really see the importance of this. So, Cornelia, did you want to add to that?

Cornelia Holzhausen (14:02): Yeah, and I think just to go back to the original question as well, around real time data, we need to give the mining companies a lot of recognition for the integrated control rooms. The moment an operator can have visibility on the data, it allows the operator as well to make better decisions. And again, we have a lot of valuable data in our projects, if I think about geomet again, where we have models and things, but somehow we need to be able to start to translate that into our operations, into our operational plans, into the future as well, to help us to actually extract the value from the resource to the optimum potential.

Kyra Meyer (14:40): If I may, I would love to add something into that. When we look at how do we really make decisions, I would love to invite people to do this as a type of cascading exercise. So, let’s just start by saying, okay, imagine that I am the underground drill supervisor. Clearly, I’m not. So, let’s imagine that. And then for me to be able to understand what I need to do, my role has a purpose and with the purpose of my role, I have goals, and I have targets, I have a short-term plan that I need to comply with. So, the way that we’re going to be capturing the data and how close to real time it needs to be, it’s absolutely tied to the fact that I need to know if I am winning or losing every shift, every day, every cycle. So, if I can get access to real time information or as close as possible, as I mentioned before, pointing towards the goals that I need to achieve and the purpose of my role, then I’m winning as a professional. So, when we start thinking about, okay, cool, I already have a solution, I have a software and integrated solution or whatever. Now what kind of data I’m going to be capturing, think about what the role, the purpose of the role and the goal of the role is, and then take it from there.

Alison Atkins (16:08): Absolutely. There are quite a few people nodding in the audience agreeing with you there. And it is around role definition, and it comes back to why. Why are you doing this and what’s the desired outcome from it, and how are you then going to do it as well? And we see real-time data and we adopt to real-time data on our smartphones and our watches seamlessly. It’s how we can embed this further into the industry as well as a whole. Right, so continuing with the theme. So, what new challenges or opportunities are emerging for companies who are able to actually harness these new types of data sets? Kyra, we’ll start with you this time.

Kyra Meyer (16:48): Thank you. Alright, so I’m going to start from the opportunity side. So, I would say I have a lot of information, I have a lot of data and the opportunity that the companies may have on having access to these types of things, it’s the velocity to make decisions. When we talk about velocity to make decisions, I’m pointing specifically into the operation. Pointing specifically into, as I said before, I have this role with these targets, with this purpose. I’m checking on the operation right now near to real time or in real time and I see a deviation. If I can have the data closer to my heart while I’m looking at the deviation, potentially I’m going to be capable of fixing the deviation as we speak. Then I will not have to wait until the end, the shift or the end of the day to be able to fix that.

And as many data we have, as more information that we have, as many opportunities of seeing different patterns and behaviours we’re going to have. But there’s a challenge associated with that. So, as leaders, we tend to get entangled into data. It’s kind of a spider web and then we start seeing ghosts, and we start seeing correlations where they should not be. So just to give an example and pass it to my colleagues here, if I say, or we see in the news, that during the Summer there were an increased number of incidents related to water, that doesn’t mean that people drown because the sun is up. So be careful with correlations on data.

Alison Atkins (18:28): Absolutely. Thank you for that. So, passing over to Cornelia.

Cornelia Holzhausen (18:32): So, there’s a lot of challenges. I think Dave already touched on it. It’s just the availability of data, our legacy systems that we have in mining. We have a lot of data, but to convert that into meaningful information, as you’ve said, Kyra, I think again, the responsibility of leaders not to get entangled in the data, but actually being the ones that ask questions and help and feed. Then cybersecurity is there. But with all of this, there’s a lot of opportunity as well. Like the bulk or shorting where we use real time data to make decisions and support our processes. We have improved capacity to run models. You don’t have to pray in the evening when your geological model or mine plan is running that there’s not a power cut tonight or tomorrow at least your model is running. So, there is so much opportunity. The question is: how do we bring the people together that’s using this information? How do we bring in experts from other industries to help us to think differently so that we can create a solution that can really have this step change in performance for the mining industry. That’s the pivot that we need to make and that we still have not got our heads around on really how to extract that.

David Henderson (20:00)

So here at Fleet Space, our CEO is a rocket scientist. So, she comes from a different industry, and she’s taken a totally new look at it, which I love. And I think to your point, I hundred percent agree. But we’re at this point where sensors aren’t expensive anymore. They’ve become way down on price and storing data isn’t expensive anymore. I remember 10 years ago we were all scared of the cloud. Now we’re not scared of the cloud. Now sensors used to cost thousands of dollars, we’re getting them for hundreds like our iPhones for example. So, we’re getting into this place where we have more and more data. And to your point, I think the key to it right now is making it usable. So, for example, a model taking months or however long, I think the real key, like at Fleet Space or my previous job at Geologic AI, we really wanted to make sure that we got the data into the user’s hands right away so they could make that decision.

So why would you want to model in two or three days? I’m not really drilling in two or three days; I’m not making those decisions in that amount of time. We have a really small season here in Canada, especially northern Canada, maybe a window of four months where we can use this data. So if I can find out that my data isn’t that great in a couple of days, then I can go back and I can rerun it or maybe I have a core and I’m drilling, and I want to know what’s going on. If I can get that data back really quickly, that’s really important. So, I think where we are is we’re in a space where there’s a lot more data, storage is a lot cheaper, keeping it in a simple place where we can access it and getting it to the user right away is how we’re going to see the industry move forward.

Alison Atkins (21:50): And it does come back to the definition of roles as well. With a lot of this is really having the roles, and we’ve seen even the role of the geologist has evolved and changed from when I was one a very long time ago. The role is there’s more around the data analytics now. It’s how you’re using the information. And I love what you were saying at the start, Kyra, it is around that agility to make decisions faster and being able to pivot or adjust right there. And then not days after a model has been generated, if it’s actually completed successfully as well.

David Henderson (22:20): In my experience when I was a junior, a lot of that data was actually lost data. The core logging data, digitising, now it’s usable and you can repeat it and check it. And if it is wrong, if everybody’s called, let’s say Alison’s calling every lithology a boot and it’s actually a rock, well later on you can go back and change everything because digitised and you don’t have to go one by one. So, I think that’s a big, big change as well.

Alison Atkins (22:45): And I actually love what Fleet Space is doing because it really is disrupting the industry as a whole and getting us all to really think about where we are going into the future. I mean with our technologies, but also our processes and the roles that we have as organisations as well. And coming to events like this and looking at all the emerging METs they’re here, but it’s really the adoption of these that we need to see because there’s still that resistance to change as well. So, I’m going to wrap it up in a second, but I just wanted to open it to the three of you for any final comments before we start a bit of a Q&A.

Cornelia Holzhausen (23:22): I think, for me, as managers and leaders, we have a big responsibility to help this organisation or this industry move forward, and not to be too connected to our decisions, but to be agile and help us to think how do we invest and support investment in not only helping data management to improve, but also this industry to move forward and be even better than we’ve done in the past.

Kyra Meyer (23:55): I would like to add, regardless of the technology that is available to us, for making any kind of decisions or solutions or whatever. I would love to recommend that for every piece of technology that we evaluate, we keep in mind that we need technology to be interoperable, sustainable and scalable. Every time that we put up a piece of technology together, we need to keep in mind the purpose, why we are doing it and what we want to achieve with it.

Alison Atkins (24:25): If you could join me in thanking our three panelists, Cornelia, Kyra, and David. We really appreciate your time and also your contribution. So, thank you very much.

Jaimee Nobbs (24:35): Thanks for listening to this episode of acQuire Connected. If you found it valuable, and we hope you did, don’t forget to click subscribe so you never miss a conversation. We would also love to hear your feedback so please leave a rating or review or share it with a colleague who you think might also enjoy the show. See you next week.

Outro (24:51): Thanks for listening to the acQuire Connected podcast channel. Find us at acQuire.com.au.