acQuire Connected episode 41: Transforming historical data into future value
In the latest episode of the acQuire Connected podcast, Nikki Arnush, Exploration Geologist and Database Administrator at Freeport McMoRan and Adam Dunstall, Operations Manager at acQuire, to unpack the value of bringing legacy geoscientific data into a modern database.
At its core, importing historical data is about making confident decisions using more complete, accurate and accessible information. As Adam explains, “a geoscience database for exploration and mining companies will contain within it all of the information that’s being used to make decisions regarding a project or a mine. And that data ultimately forms the value of that project or mine.”
Without a single source of truth to capture both current and historical data in a standardised, secure and accessible form, valuable insights can be lost in hard drives, spreadsheets or filing cabinets. “We work in an industry where there’s a lot of moving parts, a lot of software, a lot of vendors out there, and you need to be able to share that data,” Adam says. “By having that in a database, that allows that shareability… It just allows you to make those decisions a lot easier rather than going to an old filing cabinet or someone’s laptop.”
And it’s not just about digitising the data; it’s also considering the useability of historical data. Nikki adds, “Even just having something that you can query… I could say, okay, I have this district: has gold ever been assayed in it? Just a quick search in GIM Suite and you’re there.” For teams like hers, importing historical data turns static information into a powerful tool for more precise modelling and identifying future drilling and exploration opportunities.
As well as exposing new datasets that may determine where and what to mine, historical data can also uncover safety risks at sites based on past indicators and site conditions. As Adam shares, “For an underground mine, if we’re going to explore here and send people underground, there is data existing that says, okay, there’s a lot of faulting, a lot of fractures in this area. Or in the coal industry, there’s a lot of gas coming out of the coal seam in this area. So, there’s a big safety component to some of this historical data as well.”
How to approach a historical data migration project
If you’re considering a historical data migration project, Nikki and Adam’s advice is clear: plan thoroughly and validate often.
When looking at whether to bring historical data in, start by asking the questions: Will this add value to my dataset? And will it save money and time down the road?
Once you’ve decided to bring the data in, the job isn’t done. It’s time to validate it. “Make sure that it truly is unique to your database, that you can use it and that you are giving your downstream customers proper and valid data,” said Nikki.
And if you’re going to the effort of bringing the historical data in, bring as much of the dataset in as you can, is Adam’s advice. “You never know what will give you value in the future.”
To find out more about the challenges and process involved in migrating historical data into a geological database, listen to the full podcast episode.
Listen to the full episode here:
To listen to more episodes like this, head to our podcast player here or find us on Spotify or Apple Podcasts.
Read the full transcript here:
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): You are listening to the third season of the acQuire Connected podcast. If you haven’t listened to the show before, welcome. On this podcast, we interview thought leaders and technology experts who are tackling their data management challenges in the geoscientific, environmental and social performance industries, and look at their impacts on the earth’s resources, the natural environment, and the communities around them.
I’m Jaimee Nobbs, your host for this podcast. Today, we sit down with Nicole or Nikki Arnush, Exploration Geologist and Database Administrator at Freeport McMoRan and Adam Dunstall, Operations Manager at acQuire. In this episode, we explore the purpose of, and the steps involved in, integrating historical geological data into modern databases for use around decision-making in safety, resource planning and operational efficiencies. Let’s get into this episode.
Thank you both so much for joining. We haven’t had either of you on the podcast before, so Adam, would you like to start, tell us a little bit about yourself and your journey so far.
Adam Dunstall (01:19): Yeah, I graduated from university out of the UK, the University of Brighton, with a geology degree. From there, I moved to Australia, and I worked as a geologist on the east coast in the mining industry over there. Over a number of years, I ended up working for a major mining company in the role of a project geologist and got more involved with data management. Being a data collector, I evolved into more of a data manager for that company and that’s where I sort of really got a taste for data management and all the challenges involved with that. In 2012, in a mining downturn, I was laid off from that position and found myself moving to Canada and working for acQuire. I joined as a geoscience data analyst or as a technology advisor as the role’s called now, and I was involved in scoping, implementing the GIM Suite solution around the world.
I was involved in lots of data migrations; I visited many mines. I got to travel to Europe and implement the solutions in South America. So it was really fun and really opened my eyes to the nature of data management across the globe and the similar challenges that are faced everywhere. More recently, I moved into account management for acQuire here in North America, and then just recently at the beginning of a year, I’ve moved into the operations manager role for North America. So a little less hands-on with the data these days, but yeah, I do miss it somewhat.
Jaimee Nobbs (02:58): And how about yourself, Nikki?
Nikki Arnush (03:00): I grew up in California. I was a zoology major and was gold prospecting up in Northern California and thought, why can’t I make a career out of looking for gold? And so I pursued it and did my undergrad in geology. I then moved to Colorado and got my masters in mineral exploration from Colorado School of Mines. And while I was there, I interned with Freeport at a molly porphyry mine and was offered a position in exploration. So I started in 2019 in core logging and the DBA at the time had left and they asked if I would fill it. At first I said I’ll do it for six months. I want to be boots on the ground, I like to be outside, I want to do field work. I don’t want to be behind a computer all day. But I think they can tell with the people who log really slow and they’re super detail-oriented that they’d be good DBAs.
And so I started as DBA and just kind of fell in love with it. It kind of works with other things in the company too. And then covid hit and I was handling a lot more responsibilities and now it’s kind of grown. I have a team underneath me and we support North and a little bit of South America, but mostly our North American sites with anything from routine DBA tasks to building objects and managing data, it’s pretty exciting. I never thought I’d say that working. I get to go to the sites a lot, so I’m not trapped behind a desk all the time, but I really love the versatility of the role.
I love that I get to learn about sites from the data that I look at. I like the detective work like putting the puzzle pieces together and figuring out the why’s and the how’s, especially with historical data too, and trying to figure out where it comes in and how it fits in the bigger picture.
Jaimee Nobbs (04:48): I think you’ll have a really interesting perspective to bring to this topic then. Today we are looking at the process of moving historical data into a database. Why is an exercise like that so important for companies and what does having access to this information in a database actually let them do?
Adam Dunstall (05:06): Yeah, I mean I’ll start on that one and in my current role, I’m quite often just engaging with exploration and mining companies, whether they’re existing or new prospects to our business that are looking for a new geoscience data management solution. And a good portion of those companies that I engage with don’t have a current solution. So just the fundamentals of why a database is better than not having your data in a database, whether that’s hard files or a thumb drive in someone’s desk.
At a high level, a geoscience database for exploration and mining companies will contain within it all of the information that’s being used to make decisions regarding a project or mine. And that data ultimately forms the basis of the value of that project or that mine. And so at a basic level, by storing it in a database as opposed to not storing it in a database, you’re enabling a number of key things.
Security is a big one, especially these days. So with your data in a database, an organisation has control over who can view, who can edit what information, how, and where you’re sharing that information. Putting it in a database, you’re greatly increasing the accuracy as a lot of databases, especially relational ones like GIM Suite, have a lot of baked in rules around the data and what’s being captured, what’s allowed into the system. You can further configure those rules to your needs to meet organisational needs and even by deposit and by site. By storing it in a database, the accessibility of that data. So you’re able to share it with who you need. So we work in an industry where there’s a lot of moving parts, a lot of software’s, a lot of vendors out there, and you need to be able to share that data.
You need to be able to get that data to the people who need it to make those decisions that’s driving the direction of an exploration project or a mine. And so by having that in a database that allows that shareability amongst the organisation externally. It just allows you to make those decisions a lot easier than going to a filing cabinet or going to someone’s laptop. But bringing it back to historical data, organisations like Nikki’s, they want to have the most complete dataset possible to allow them to make those decisions that ultimately drive the direction of the projects and the minds and the value of the company.
Nikki Arnush (07:37): I think with Adam’s “shareable” comment, even just having something that’s queryable. So, a lot of the Excel CSVs, if you’re lucky enough to have data in that format, you can’t compare one against the other necessarily. Having something where I could say, okay, I have this district: has gold ever been assay in it? Just a quick search in GIM suite and you’re there, but having something that’s queryable, having something that’s modellable, if that’s a word, to be able to use it in downstream processes, it makes it so much easier. We’re finding other areas in the company where data is in spreadsheets and we’re like, why don’t we bring it in? It’s never been done before but we can query it, it’s in a secure location, it can be used in downstream processes, it just is the solution for data for us. And in terms of it can be everything from modelling to future drilling and exploration, which is what we do, a vectoring tool, or it could tell us also where not to go. So it’s why we have it if we can’t use it. Who has the time to go through a filing cabinet one-by-one now?
Jaimee Nobbs (08:54): Is it something that you are seeing a company kind of need to go through? Is this an industry change over the past few years or is this something that you’re just seeing companies want to try, want to look back on historical data to uncover more ore bodies?
Adam Dunstall (09:10): Yeah, I mean there’s always sort of the natural uncovering of historical data, but also with the trends in the mining industry as well and properties changing hands, mines changing hands. Sometimes, there’s just the necessity of moving that data from whatever format it was in, whether or not that’s historical or another solution, having it all in one central place that you can query and report on. It’s always a cyclical sort of industry in the way that these projects are changing hands. You’re handing over the data, you need to put it all in one place to be able to do your models, to drive your decisions, to share it with others in the organisation. So it is not new, it’s always happened, but definitely with new sort of data types coming online, advances in technology, there’s different data sets now and tools available that you can potentially use this data with. So, certainly there’s an increased focus on different data types that potentially weren’t considered valuable in a database.
Jaimee Nobbs (10:16): You’ve made a few interesting points there, Adam. I’m just curious, how does a company that is working with historical data that don’t necessarily match up with the data sets now, what problems does that kind of present an organisation?
Nikki Arnush (10:36): So much of it is validating, right? But you have to make a decision. Say you have existing newer data that’s in this beautiful format and you have all the fields there to bring in the data, and then you have this historical data set that maybe things are lumped together. You say that this mineral’s present, but you don’t have volume percent, weight percent, et cetera. One of the biggest challenges is are you going to bring it in and generalise it or are you going to not bring it in? Will it muddy the current dataset?
Adam Dunstall (11:09): Yeah, and it’s validity. It may have been important then, but is it important now? Even we’ve seen in implementations I’ve been involved in, they may be looking for one specific commodity or mineral, but prices change, industries change, and now what was considered an accessory mineral is now what we’re targeting. And so it’s capturing it for different reasons. There may have been data that wasn’t considered valuable to a previous company or a previous owner, and now all of a sudden that data is valuable because the targets have changed.
Nikki Arnush (11:45): You look at what the assay back in the day, and it was a lot of times just above a certain grade and now we can be economic at much lower grade, so do we have assays? Was there a mineralogy that was important there that just wasn’t recorded because they didn’t care? Is it a complete data set or was it complete to them, but could be more complete now?
Jaimee Nobbs (12:05): It must be hard trying to decide what to keep and even collecting data now. If we look back in 10 years time how that’s going to change as well. What we deem important now is going to be completely different. In terms of types of historical data, what types of data have you worked with Nikki?
Nikki Arnush (12:25): Yeah, so the most basic handwritten logs, sometimes pictures you get to see with the logs. Some of the logs I’ve seen go back to the 1910s for us. Beautiful handwriting sometimes on those assay reports. So handwritten logs and assay reports. Sometimes we only have scans of those documents, underground maps, we went through a project validating holes and we had a data set that was questionable. So we started going back and found some underground maps from about the 1980s to try and validate – do these holes exist? With that, you can find more holes and more data, maybe some maps on those levels. Model exports from pretty old modelling systems where it’s very basic field names. Even we found survey discs that were like films almost. I’m not sure what the technical term is, but it’s like a film with a survey information on you hold it up to the light or on the light table to read it. So that’s data too.
Jaimee Nobbs (13:26): So it can come in any format?
Nikki Arnush (13:27): Any format, yeah. So those are the main ones that I’ve worked with.
Adam Dunstall (13:31): Touching on your pictures thing there. The one that I always used to really love coming across was the old field sketches, geologists back in the day, like the beautiful outcrop drawings is like a lost art. I was never very good at that sort of stuff at university or in the field, but from the early 1900s, some of those sketches are just really, really nice.
Nikki Arnush (13:55): Back when you still had outcrops there. So it kind of takes you back to imagine what that landscape looked like before.
Jaimee Nobbs (14:02): So, you’ve mentioned all the different types of historical data that you work with and a lot of them aren’t really data sets like you mentioned that we work with now or types of formats of data that you work with now. What are some of the key challenges that companies face when they decide to import historical data from all those different types of formats that you’ve mentioned into a database? How do you go about that?
Nikki Arnush (14:32): Think one of the first questions you ask is, is it valid? Is there validity to it? Does it make sense? If it’s a copper deposit, are they seeing copper minerals? Usually, it’s pretty spot on. I think one of the big questions for management is will it add value to bring this data in? Do we already have drilling in those areas? Do we already have data sets that are similar that could supplement it with more recent technology to acquire it? Bringing in historical data is very time-consuming.
Even with OCR and scanning technology coming along, will bringing it in add value? Will it make our data set better? And so one of the first questions they ask, is there a way to validate it? If we have these handwritten logs, do we also have an assay set that kind of correlates to say the mineralogy they’re seeing? Do these holes have surveys? If we don’t have any survey information, it’s informational for what was done, but we can never model those holes necessarily. So I think it’s a balance of the time and energy it takes to convert it into an importable format. I know intern projects for our company in the past has been hand-entering some of those historic logs. We haven’t done that in a while. But it’s not out of the question. Yeah, I kind of mentioned earlier, say you have a bunch of sulphides and we came across one where they just said sulphides, yes. It doesn’t tell us what specific ones. Whereas another dataset, we’ll have it breaking out. Is there chalcopyrite? Is there bornite? Is there chalcocite? et cetera.
Is it complete and what do we need to bring it in? And also with the OCR that we found, we’re doing a project right now where, we can get it into Excel or CSV, but say we have 30 holes from one company and then 120 holes from another company. Those formats from the beginning aren’t the same, so it’s not spitting it out in the same format too. So then we need to build different importers for each series of these holes. So I guess going back to what goes into it and whether we bring it in, is it complete? Will it add value? How do we want to bring it in the most time-efficient yet complete and right way? And then what will be done with it after?
Jaimee Nobbs (17:04): In terms of future technologies handling historical data, does that present challenges? Does that involve a lot of manual processing still or is it once it’s in the data set, you can kind of use the same systems and processes into the future using that historical data?
Nikki Arnush (17:25): At least using acQuire GIM Suite. I think once it’s there, it’s okay. Then it’s really deciding how do we want to rank this? What priority scheme do we want to put on it against other similar values. If we bring a bunch of stuff in and then it’s not modelling, right? Okay. One of the first questions, what coordinate system were they using? Are they not using the one we thought they were using? So I think it can be used in downstream processes, but you have to take into account that it is historical data and technology and best practises were not what they are now essentially. So you give it a little bit of less of a weight compared to newer data. With some assay methods they had heat or agitated a different amount, then it can produce different values than the methods now so one thing we found is if we’re getting values way higher than expected, what were the assay methods back then? And so that’s a situation where we weren’t expecting the data to be invalid or have a calculation curve to it essentially, but it’s another factor we have to take into account for downstream processes.
Adam Dunstall (18:36): In your role, Nikki, how often are you presented with historical data sets or just, hey we just found this. Can we put this into GIM Suite? How often is that actually happening?
Nikki Arnush (18:50): I feel like it kind of comes in waves. I was working on a project between 2020 and I guess about end of 2021, where it was a lot of historical data and that database hadn’t been touched in five or six years. So it was going in, validating, and it was a huge effort for two years and just changing priorities.
Modifying sample priorities to make everything work. And then it was kind of quiet for a while. I’ll get questions about, okay, did this site ever assay for this and use the database coupled with scans of assay logs and drill logs. And now we have a kind of big project right now I think some of your Nova Network partners are helping with. They’re helping bring some data in and we’re also my team’s going through it and validating it and kind of organising it. And right now it’s with a company that produces OCRs, so document scanning technology. So in waves as needed. We decided to look at an area that hasn’t really been looked at for exploration and so it’s kind of like all hands on deck to see what historical data we have. I hope one day it’s all in, but as people go through old warehouses at mine sites, we find more documents more core.
Adam Dunstall (20:17): Yeah, it’s just prioritising that as you say, what’s valid, what’s going to make a difference to what we’re reporting. But yeah, definitely on the challenges there. Time is the big one. Being on the other side and working for acQuire and implementing GIM Suite, it’s always the time. People always underestimate the amount of time and you don’t want to pay for us to migrate all this data and utilising the Nova network partner consultants that specialise in this sort of digitisation of paper logs. Some of that can be done after the fact. Let’s prioritise what’s important right now. And yeah, some of this historical stuff can just be sort of a future phase. But yeah, the time is the big one and the scope creep on data migration projects is definitely real. It’s like, oh, I just found this folder on so-and-so’s computer. Oh, we just went to the warehouse and we don’t have these drill holes from 1962.
Jaimee Nobbs (21:20): It must be quite cool seeing those data sets coming in though. And I would guess that even though it is a time-consuming process, there is a lot of information that you can get out of it. What sort of decisions are you driving using that historical data? What sort of decisions can you make with access to that information that perhaps you might not have been able to make more broadly?
Nikki Arnush (21:42): It can definitely tell us if it’s an area that maybe not a lot of drilling or exploration has been done on, then hey, we had some sniffs here in the sixties. Do we want to plan a drill programme around that? Is it something we want to go back to? Or maybe you find a data set and they did pretty tight drill hole spacing and oh, there’s nothing there. I think the main tool is telling us if we’re not already mining that and have recent drilling, is that somewhere we want to go explore again or it’s telling us, hey, we don’t need to plan a drill program there. It can also tell us, I am pretty big in vectoring tools. Can it tell us, okay, there was nothing this way, but hey, they had two holes out this way that had this mineral, which wasn’t an indicator for them back then, but now we know it can be an indicator.
Adam Dunstall (22:34): Yeah, I’d say from when I worked as a geologist in the mining industry, sometimes just around safety as well. So I worked in the coal mining industry and just uncovering historical data that would suggest, especially in an underground operation, okay, there’s a lot of gas here. So it just exposes new data sets. It’s not all about I guess the commodity or what you’re mining or what you’re exploring for. There’s also those fringe, not that safety’s fringe, it’s very important. But that data brings that to the forefront. It’s like, okay, well if we’re going to explore here, if we’re going to send people underground here, there is data existing that says, okay, there’s a lot of faulting, a lot of fractures in this area. In the coal industry, there’s a lot of gas coming out of the coal seam in this area. So there’s a big safety component to some of this historical data as well.
Jaimee Nobbs (23:33): So, it’s not just about where you mine or what you mine, it’s also how you mine and perhaps the way you go about it in the area. Yeah, so that is quite fascinating, it’s not just the commodity. And so if we look at the actual process of transforming historical data from paper logs or PDFs into a database, what does that process look like? You’ve kind of touched on it already a little bit, but what was your experience doing this exercise and then yeah, I guess you’ve talked about data cleansing and validation. So, what was the actual process like? Nikki, perhaps you can start and then Adam, if there’s anything else you want to add to it as well?
Nikki Arnush (24:21): Yeah, I think one of the first things is looking at what you have. I think organising it. How complete of a data set do we have? Like I said before, do we have logs, do we have assays? Do we have surveys? Do we have caller information? Do we have existing fields or do we need to come up with new fields to put this data in? Will it be lumped? So I think the first thing is lots of validating, just taking a step back, looking at your whole data set and then how we bring it in and then what will you do with it after from there. OCR if you have ’em, interns if you have them too. Yeah, I think OCR and importers are the way to go now directly into GM Suite over hand entry by any means. We all know humans make mistakes even entering into acQuire’s GIM suite.
So the more we can reduce that, the better. Yeah, one thing that’s cool with the company that’s been around for so long too is talking to other people that may have worked on that project or known people that worked on that project to say, Hey, is there any other information that you have or that might exist. Most of our sites are open pits now, but even looking at those underground maps to say, how did they plan this? Did they forget to put a negative on a dip? And it’s really an underground positive dip hole, and then bringing it in and then once it’s in the database, your work is not over yet. Then validating it in the database and querying it and see what makes sense there and then throw it into a model. Does it make sense in 3D space? What am I seeing here? So again, validating, organising, and then validating again,
Adam Dunstall (26:08): There’s also a large component as well around the metadata associated with getting this data into the database. So when we, at acQuire, rock up to site to implement a new site and migrate some historical data, there’s a whole lot of translating that needs to happen around, so you’re calling this Field X, but it’s been recorded 10 other ways historically over a hundred years. So, what do you want it called? How are we translating how company A called it to how you want it displayed in your database? And so there’s a large portion of having those decision makers involved to say, okay, when you see this field, this is what we want to call it in GIM Suite or in your database. And so yeah, all that metadata associated with the data you’re bringing in needs to be in order and you need to have someone available to make those decisions as well around, what are we going to call this? Just because so-and-so called it this, is that what we are calling it? Geologists are human so, to take that ambiguity out of what the data that’s been recorded, we have lookup fields, we have the metadata there in the database to do that. But yeah, consolidating that and getting it into the database is a lot of work.
Jaimee Nobbs (27:31): One thing I found quite interesting there is just the involvement of other stakeholders. I think that’s something I perhaps haven’t considered is right from people that were mining back in the day, those geos that might not be on the tools now, but being able to go back to them and ask them and then, right through to decision makers, understanding what we want to call things now. I think that’s an aspect of working with current data as well, but it would be very interesting working with historical data across a number of different stakeholders. What advice for companies that are perhaps looking at doing a project like that, moving historical data into a database, what advice would you give people or companies looking to do that activity? You’ve mentioned a few considerations about validating the data, but is there anything that you would, any particular advice that you were given or that you would give to someone doing it for the first time?
Adam Dunstall (28:35): I mean, for us at acQuire, when we come in to do this work, we have a pretty rigid project management plan. There’s certainly a lot of considerations. A lot of this stuff that we’ve been talking about needs to be done before we even start the work. So there has to be a plan, there has to be a budget to do it, there has to be a timeline in place. There’s a lot of deliverables. So talking from sort of the acQuire vendor side is probably very different from Nikki’s side as a mining company. But we have a plan, we have a budget, we have a set amount of data that we are looking to migrate over a certain timeframe. And as I sort of mentioned earlier, you don’t want us at acQuire to do all of that work for you. We are here to deploy a GIM solution.
And so having, whether it’s Nova partners or consultants, there’s vendors out there that do a lot of this translation from PDFs to digital. Having all of that in order, ready to go for when the data migration happens, you need to plan ahead and budget accordingly. Advice: it will always take longer than you expect, it seems. There’ll always be changes. There’ll always be new data sets that come out of the woodwork that someone thinks is important. And it’s about sort of managing those expectations and triaging what is important and what you do want migrated. But yeah, it’ll be worth it in the end. Stick with it. Nikki?
Nikki Arnush (30:10): I would ask the question, will it add value? Will this add value to my dataset? Will it save money and time down the road? And I think a piece of advice is if you find those logs, bring in whenever you can. Just because you might need a portion of that now, you’re not doing anything with alteration. Let’s say everyone uses alteration, but let’s just say for example, they’re not using alteration. Like if it’s there, bring it in, you’re working with the data set, you might as well take whatever you have and then you know, it’s complete. Whatever I had in these drill hole logs are now in my database and queryable, and again, check the data. I was dealing with a site where it had gone through quite a few hands and in the eighties, and I don’t know what sort of database or modelling software they were using, but they had brought in what they thought was a brand new historic dataset, renamed all the drill holes, but with a similar nomenclature to the previous.
So then we had new drill holes written on these logs, which matched the drill hole in the database, but was not the same drill hole. And then what we found is that we just started validating data and heard that there could be duplicated holes. So I exported and looked at everything with coordinates within two degrees of each other, and this company had imported this huge data set of holes where I think they thought it was surface negative, vertical or slightly angled holes. And it turns out what they had. I started looking at it and the assays matched the other holes with similar coordinates, but in the opposite direction from bottom of a hole to top. And so they had brought these holes in thinking they had a negative dip, and really they were already in the database as underground holes with a positive dip. So will it save time and money and then validate it to make sure that it truly is unique to your database, that you can use it and that you are giving your downstream customers proper and valid data.
Adam Dunstall (32:19): Yeah, definitely a double-edged sword between bringing it all in, what adds value, but you don’t know what’s going to be of value in the future. But if you’re going to the effort of digitising stuff and you’re paying someone a service to do that, you might as well bring it in at that point. There’s no point bringing in part of it, as you say, and you never know what will give you value in the future.
Jaimee Nobbs (32:48): Your future self will thank you. It really does sound like your role is a lot of problem solving, a lot of detective work, like you said, a lot of understanding what’s going on now, what perhaps happened in the past, was it new technology, who was involved. It’s a lot of just putting the pieces of the puzzle together to tell a story and make those decisions now. So it is really fascinating hearing about your experiences in doing that. We will leave it there though. But thank you so much for sitting down and chatting with me. I really appreciate it.
Thanks for listening to this episode of acQuire Connected. If you enjoyed today’s episodes, we’d love to hear about it. Please like, share and subscribe on your podcast player and leave a review.
Outro (33:43): Thanks for listening to the acQuire Connected podcast channel. Find us at acQuire.com.au
