Hypercube Podcast Transcript

Title: From Data Director to Freelance Consultant

Host: Adam Sroka
Guest: Elizabeth Hollinger

Intro: Welcome to the Hypercube podcast, where we explore the cutting edge of data and AI in the energy and utility sectors. I’m your host, Adam Sroka, Founder of Hypercube, a strategic technology consultancy supporting asset owner operators, traders, route to market providers, and energy services companies unlock the power of data and AI.

Join me as I sit down with experts from across the energy industry to uncover how they are navigating today’s data driven challenges. And without further ado, let’s get into today’s episode.

Adam Sroka: Hello, and welcome back to the Hypercube podcast. I am chuffed to be here today to be joined by Elizabeth Hollinger. Elizabeth. 

Elizabeth Hollinger: Yeah, thank you. I’m delighted to be here. Good to get a chance to catch up. So yeah, I’m Elizabeth Hollinger. I am currently a freelance data and technology consultant. I have almost 20 years of experience working in data and tech across industries, which definitely makes me sound older than I feel.

Adam Sroka: So really good to speak. And for those that maybe don’t know, some of your background, you were data analytics director for a fairly sizable data team at Aggreko, right? 

Elizabeth Hollinger: Yeah, that’s right. Yes, so the role I had before I decided to go freelance, I was Director of Insights at Aggreko, which is a sufficiently vague title, but really what it meant is that I looked after the data and application development teams for our global business.

So that included our data science team, who built and maintained our machine learning models, data engineering, who managed those pipelines and our overall data platform, Our BI team that were comprised of business analysts and also BI developers who created our operational reports to support our global business and our applications development team who were our software engineers responsible for creating and maintaining our mobile and web based applications.

So the reason I was called Director of Insight is because we had an ambition to become an insight driven organization. And really to create AI driven applications, bringing together the capability of our data and dev team to deliver insight quickly to take actionable insights from it. So that’s why I was Director of Insight, because it’s not always an obvious title given the role that I performed.

Adam Sroka: And just got a whizz. I’ll probably mess this up, but for those people that don’t know Aggreko, if you’ve been to like a large music festival or like the Commonwealth Games or something like that, you’d almost certainly have been powered by an Aggreko asset. So it’s like mobile on demand power. 

Elizabeth Hollinger: Yeah, so temporary power generation really is a, Aggreko are probably, if not the world leader, certainly in the top kind of three or four leaders in the world to do this.

Yeah. So yeah, the core business for Aggreko would technically be providing power in areas where a network of power is not available. So for example, if you’re in the North sea and you’re drilling for oil, then you need to have a lot of power to enable those drills and you can’t just connect to the grid and equivalently.

If you’re in the developing country, that doesn’t have the infrastructure available, then we provide generators to be able to support hospitals, other infrastructure, to, Revive Power for various communities. And also the kind of, most obvious place where you would see Aggreko generators are in those big events like Formula One, the Super Bowl, Commonwealth Games, the Olympics, etc.

So that’s typically where you would see that on a day to day basis. And now that, that you know the name Aggreko, if you didn’t before, I am certain you will see van generators at the Christmas markets upcoming in Glasgow and Edinburgh. You’ll likely see generators there. So yeah, and not always obvious because they’re not a B2C business but very much kind of prevalent across all countries and all industries.

Adam Sroka: It’s a really interesting business because you have all the headaches associated with energy generation, fuel prices, so on and so forth. Plus a logistical nightmare, right? 

Elizabeth Hollinger: So it made, it was a complex business and a really interesting one for where you could apply data to be able to solve some of the complex challenges.

So we used to talk about it in three different buckets, if you like, where we tried to use data to drive value and really to enhance and augment human decision making across the whole organization. So the first place we looked at were our generators themselves. You’re right, there were lots of different kinds of generators that ran on all different types of fuel.

But they all had to be maintained. And so what we did in our first focus, while I was there in the team, was to think about how do we make our assets operate as efficiently as they possibly can. So how do we predict when a part might fail so we can replace it before it fails and causes further damage within the machine and take proactive action to maintain our assets so that they’re in the best condition, they have the best lifespan.

So the first thing we really thought about were how do we best maintain the assets that we own to make sure that we get the best life cycle out of them. The second thing that we used to think about are how do we get our assets from where they are to where they need to be. So I’d record a huge organization and they operate in 200 countries pretty much worldwide.

And so we had to think about, logistically, what is most efficient for us to do with this asset as it comes off one job before it goes to its next job? Where should we, which depot should we send it to, to be able to do the repairs and the maintenance, and then where should we send it after that? So that in itself was a big logistical challenge to try and solve.

And so we started to think about how we could use our data to improve that and to say how do we get our assets more efficiently from where they are to where they need to be? And that involves looking at our sales pipeline as well as our operations and where they were. Then the third thing we used to look at are where should we sell our assets?

Where should we rent them? Where are our customers? Then we used to look at our sales pipeline and our marketing pipeline to see who are our most profitable customers. What’s happening around the world that might provide opportunity for us to be able to provide power and which customers should be target and equally, which customers should be not like, where do we think best opportunity as this?

So we used to really think across those kind of 3 different areas across the organization. And really embed data and insight at the heart of that decision making to make sure we can make the best decision that we can with the information available to us. 

Adam Sroka: No mean feat, right? And I want to get into, because you achieved some pretty special stuff while you were there, but I want to just get into where did the driver for that come from?

Did that come from you or was it top down, bottom up? Like, why, like who, who wanted it to be data driven and how did you make that happen? 

Elizabeth Hollinger: Yeah, so I think there was an ambition within Aggreko to be data driven. And to be clear, even before I joined, Aggreko were always a reasonably data literate organization because generally it’s an organization full of engineers, people who like data and want to use data.

So we were always really good at using it. And when I came in, what I really wanted to do was to extend the sophistication and what we were doing with our data to embed machine learning, artificial intelligence, being a bit smarter about how we use it, and also enabling self serve across the organization to make sure people had access to the information that they needed.

But the way that we really started was to see, what is it that we’re trying to achieve as an overall organization within Aggreko? What’s our overall strategy? And what’s our strategy for growth? And what we did then is we essentially mirrored our organizational strategy with our data strategy. And we said what we want to do is to accelerate the delivery of value of our organizational strategy by using our data.

So in some ways you can see it was top down. We just took what the direction was and we targeted our data projects to things the organization were already trying to do. And then the bottom up part came how are we going to do that? How do we structure that? How do we think about it? And to be clear, the reason that we had best success within Aggreko was because of the collaboration that we had across our technical teams, our data teams and our subject matter experts, whether that was in finance and sales and engineering. What we really drew down from our business knowledge and acumen and joined that with our data knowledge and acumen, we could really get to the heart of the problem we were trying to solve and then how we could use data to solve that problem.

So I would say it was two senses, but it really started with that top down approach of What is the organizational strategy and how can we use data to deliver that one? 

Adam Sroka: Sounds like a lot of meetings and trying to wrangle different stakeholders and getting people. On board is like that the bulk of the work and I imagine you move into, because I remember there was a large headcount as well, like these are large teams you’re talking about, like hundreds of people, right?

You end up in that teams of teams space, do you? 

Elizabeth Hollinger: Yeah, so Aggreko has 7000 employees around the globe, so a reasonably big size. But actually also a reasonably lean data and dev team, a small team of excellent people. That’s what we had, and it worked really well. So in about 50 in total across the data and dev team, delivering against all of our various projects.

And yeah, we had really high demand for what we were trying to do. So one of the ways we tried to stem and to manage that demand was setting up an Insights Steer Co. So we had lots of demand and part of our challenges, our demand came from different parts of the business who weren’t, it wasn’t always obvious to them, it wasn’t always clear where the effort was being placed, or it wasn’t always visible with the products that were being developed.

So what we did is establish the Insight Steer Co. I think I’ve been in the organization maybe three, maybe six months. To essentially serve two purposes. One is to communicate and make visible all of the things the team were doing. Because we were creating some models or even some standard operational reports in one part of the business that actually would also have been helpful in another part of the business.

So we really wanted to share that out to make sure that everyone had access to the information that was available and it could be put to use across various different teams and for different purposes. So that was one reason we set this to your co op. And the second reason was to be able to prioritize the various demands that come into the team.

Because there was a kind of feeling in the organization that a request was made and into big black box and maybe in three months, six months, a year’s time, something might be built at the end of it. We wanted to make it really transparent what we were working on and why we were working on it.

And to do that, what we did is we got a group of senior stakeholders who were at my peer level across the organization. So other directors of technology, directors of operations, directors of finance. to meet every quarter, to review the list of requests that had come from their team, and to see which of these things do we think is most important, and we looked at that from two lenses.

One is, how much value do we think it’ll add to the organization, and how do we measure that value? And the second was, how easy do we think it will be to do? Because some things would have been hugely valuable, but by themselves have been a two or three year project. And other things, less valuable, but might have taken us three months.

So we weighed up both of to decide what our portfolio of work would be for the next three months. We’d come back in three months, reflect on it, share what we delivered, and then review again what we want to do next. So really it was about trying to manage the various stakeholders who were spread across the globe, all with different priorities, and we were one reasonably small central team trying to manage that demand.

And that’s just one of the ways that we did that and worked collaboratively with the teams to do 

Adam Sroka: so I often talk about how I think, whether you like it or not, in, in data and data science kind of roles, you end up being a bit of an internal consultant in one way or another, because there’s an education journey.

And you’re often trying to bridge the gap between like business need and the technical domain. You have a Background on being a consultant. How much do you think that helped in your role in doing all that wrangling? 

Elizabeth Hollinger: Yeah, no, I think it massively helped actually coming in from, Working as an excellent external and other organizations and helping them to understand what they could do with their data and then applying it and that can reveal internal setting.

And actually, one of the first things that I did when I joined the Aggreko, I think within my first three months, I really just wanted to understand the business prior to joining a Aggreko. I had worked in lots of other industries, predominantly financial services and then public sector, but I had never touched the energy sector.

So just didn’t really understand that hadn’t really worked in engineering. So I spent probably the first three months really trying to understand the business. What is it that we do? What are the pain points? How are people using data? How else do we think we could use data? And part of that was educating them too, because as much as the organization was data literate, they also varied, different parts of the organization were at different levels of understanding of the art of the possible.

This is going back to 2018, when I joined the Aggreko. I think we’ll probably come on to later talking about the pace that technology moves at and where we were then versus where we are now. But really people were starting to pick up more and more, starting to use cloud technologies, and starting to think about the art of the possible and what would be available then.

And it’s at that point we really started thinking about what’s our, overall AI machine learning strategy, how do we apply more sophisticated analysis that we’re doing at the moment. But to do that, we had to first of all, convince our organization that it was going to be worthwhile, that there was value in it for them if you were to embark on being able to do this.

So actually within those first three months, I spent a lot of time traveling around and meeting different people and different operating units across the business. But also running workshops, educating them on the art of the possible and understanding from them what are your biggest pain points. And for that I used, some workshop styles that I had learned within consulting.

I showcased some examples from other industries that were online and other case studies. and before we had our own to showcase and it was just really powerful to really bring to life the type of thing that we were trying to do and also trying to prove that value and where it could be possible within Aggreko

Adam Sroka: and what was your view i mentioned you’re a sympathizer because Consultants sometimes have a bad reputation, it’s a bit of a dirty word in the energy sector sometimes, but I imagine you’re more sympathetic.

Where do you think the natural fit for a consultancy or a partner is in organisations, like that kind of build by partner? 

Elizabeth Hollinger: So I think it depends. I actually think it’s been really interesting sitting on both sides of the fence, both as a consultant going in to advise organisations on what they could do, And then setting, owning that piece on the end of it to see actually this is our strategy.

This is what we’re going to deliver and having the responsibility to go and do it. So I think there’s probably a couple of areas where consultants are really helpful within organizations. I think the first one is in keeping up to date. In the market and what’s going on, and there’s lots of different places you can do that.

You can go to conferences and events, you can join roundtables, etc. If you’re starting to think about your strategy and where you want to go in the next step, generally consultancies are good places and good people to help you be able to develop that and help to structure your thinking around that.

Because not only do they see what’s happening in the market, but they also have a view of what’s happening with other clients like yours. So really once you, okay, Aggreko, a similar organization are doing this, and this particular organization are doing that, collectively, they will have that view. And that’s also really helpful just to think, make sure that organizations are thinking about all the kind of future looking trends that their peers are thinking about too.

So I think that is a really helpful thing. And I did notice. I had to work harder in industry at being on top of trends that I did in consultancy because it just happens by osmosis, by talking to peers, talking to other clients, like you just absorb all this. But actually when you’re in an industry, I think you need to make a concerted effort to go and do some of those things.

It doesn’t just naturally happen. So I definitely think that’s one thing. And I think the second area where consultants can be really helpful is in any kind of transformation project. That can be technology implementation, that can be a new type of operating model, could be a lead off strategy or a new process implementation, doing some change management.

And the reason I think consultants are good at doing that is because that is typically something that an organization does. Once. One new data platform per CDO, for example. One piece of chain work per CEO, whatever it might be. So you typically do that once in your tenure at an organization. But consultants do that multiple times a year.

Because that is the thing they do in different organizations. So again, there’s lots of learning to be taken from people who have done that before. And then they can take that learning and apply it to your organization and tailor that really to what it is you’re looking for. So those are the two kind of areas that I think that the consultancy can be really valuable.

Adam Sroka: No, I totally agree. I think the only other one I often cite is risk transfer. If you just want to transfer the risk of a project onto another party and for a right for the right price, right? Like that’s quite possible with it. Yeah, absolutely. I guess the last question about Aggreko, for me, you talked about getting in front of the technology and actually the transition 2018 pace of technology, but you achieved some pretty big stuff and were like a golden case study for Microsoft for quite a while because you were really forward thinking in the platform.

Could you expand on some of that for the audience? 

Elizabeth Hollinger: So I think I did Aggreko around about six months or so at the time. And we had just lightly reorganized the data team to make it a little bit smoother in terms of how it operated, bringing different parts of the team together, like physically sitting together, working a bit more closely, doing more consistent joint team meetings, et cetera, learning from each other.

And then we started to think about the tools and technology that the different parts of the team were using. We just started to grow out the data science team, and we had established a data lake within Azure, I guess we were a big Microsoft host, so we used a lot of Microsoft technologies. So we built out this data lake in Azure, which at the time was running maybe two or three machine learning models.

Those kind of proofs of concept that we were testing out with the business to try and prove the value and doing some of this more sophisticated analysis and getting really good traction with it. So we thought, okay, we know this is going to grow. But at the same time, we also had our traditional on prem data warehouse that was pretty mature, had been running for maybe 10 years.

And we support the presentation of reports to our business, so typical operational reports, either via Power BI or typical paginated or tabular reports. We started putting more and more data into our data lake, whilst we were still maintaining our data warehouse, it was peaking at the seams.

And I started to think about there must be a better and more efficient way to be able to do this. We shouldn’t need to run to data platforms and we need to do something about this off prem data warehouse because actually it’s coming to end of life. It will soon need servers replaced, like what are we going to do about it?

And how do we upgrade it to future proof it? So I started to do a bit of investigation, speaking to various industry experts, went to the Gartner conference, spoke to some CDO peers to understand what they were doing. And then come up really with our cloud first strategy to create a modern data platform, which would be a single place where we store all of our organizational data for multiple purposes.

So to enable self service across the business for people to do their own root cause analysis, etc. And when the data to deliver our operational reports to support our machine learning models and to deliver data and insight via our applications. All from one central hub. So when we started talking about this and spoke to a few different solution implementation partners to see how should we do this and how could we approach it, some did suggest that we just lift and shift our data warehouse and we just put it into the cloud.

I said that’s great. That solves a hosting problem, but it doesn’t solve any of the other things that we’re trying to solve, which is about getting consistent data. We’re modifying the data only once, we’re creating it once, we’re cleansing it once, we have one set of pipelines. If we lift and shift, we’ve just got two sets of pipelines running into the cloud, but nothing else is sorted.

After a while, we decided to fully redesign what we had within our data warehouse, and we redeveloped that and redeveloped all of our data pipelines, so they were flowing consistently into the cloud. So essentially what we had, and it probably took us a couple of years to deliver it, was this one single modern data platform.

It took data from all of our enterprise applications. Our IOT data that was streamed in near real time, every probably 30 seconds or so, into our data lake. And we also ingested some external data, such as weather data, etc., into the data lake, curated consistently through our bronze, silver, and gold layers, and then presented that to the various applications that needed it.

I should probably pause there and say, we created, I think, what was a really neat solution and which met the needs for Aggreko. But what we created would not be possible in every organization. And we were very aware of that too. So Aggreko, although they were a large organization, 7, 000 employees operated in, 200 countries 2 billion dollar business.

From a technology standpoint, it was actually relatively simple. We had an ERP system, a CRM system and essentially an IoT data. And that was it that ran our business. We weren’t a financial services institution like a bank’s got a bank’s got a bank’s got a bank’s that’s got all of these legacy systems that are sitting behind that are really difficult.

to then bring into one place to present information out. We weren’t in that scenario. So for us, it was actually reasonably simple to be able to do that. If any of the team were listening, it was not an easy project and not a simple project in the end. But it was a thing that was achievable and we were able to do because of the way that the data within Aggreko were structured and the few systems that we had, which basically ran the business.

So yeah, but it was good. So we got there in the end. 

Adam Sroka: And just for, just to remind people in the context, so it’s 2024, modern day, a platform, we’ve got all that post modern day, a platform and all of a sudden they’re like, marketing, blah. In 2018, that was like, not, it hadn’t really been done.

People were just starting, like the kind of marketing diagrams were appearing on Microsoft and things like that, but finding real case studies. Yeah. The Lyft experience wasn’t there. So it was some achievement. 

Elizabeth Hollinger: Yeah, no, it was. And I think it was all driven by we, we had to do something anyway. And we were saying if we’re going to have to do something, let’s do strategically the right thing.

And let’s just do it once, rather than lifting and shifting, then trying to bring together. And I was like, let’s just do this once and we do it right. And then it’ll work from then on in. So yeah we did it. And at the time, yeah, we did, we, and we ran a number of case studies with Microsoft and with Databricks themselves, because we were a really early adopter of Databricks.

Which really was the heart of the platform and how it ran. And so even with them, we were one of their first customers who had really implemented something like that at scale that was supported by Databricks running at its engine. So yeah, it was great. It was a really good project to be involved in.

And tell me. 

Adam Sroka: Hey, cool. I’ve seen the Databricks cluster at Aggreko. That’s yeah, good font to look at. The, yeah, I’ll get some stuff done. Okay, enough about Gricko. What are you up to these days? 

Elizabeth Hollinger: So now, yes, I finished up with Aggreko in 2023, as I would have intended to leave. I had Matt, my second little girl, and after that, I had a bit of a choice to make about what to do next.

Whether to take up another industry role, leading another data team, or to try and do something more in the consulting space, that kind of project type work. So I decided to go and become a freelance consultant, really working to support organizations to best utilize their data, to drive insight from it, and to make better decisions.

So really I’m focusing on helping organizations with an overall strategy. What are you trying to do with your data? What do you want to achieve? What should your operating model be? From small organizations, how many people do you need to be able to utilize your data? Do you start with one? What skill set do they need to have?

To large organizations, what does your overall operating model look like for your data function? What does your data governance look like? And what tools and technology do you need to be able to implement a really effective data function and to really get best value out of your data? music ends So I’m doing that now working mostly in Scotland, which I’m really enjoying, and yeah, enjoying again having a bit of variety of work, working across different projects and with some different people.

So it’s been good, exciting. 

Adam Sroka: Really cool. And I guess that’s terrifying for some people. Any advice for people that are a bit nervous to make the, the plunge into the abyss of freelance? 

Elizabeth Hollinger: Yeah, because I suppose I was a bit apprehensive about it as well, and thinking How do you find work and, who are you going to work with and are you going to enjoy it?

I have to say it’s not, we’ve not been doing it for too long, but actually it’s been really enjoyable, I think. I think having a bit of autonomy over the types of projects that you work on and the people that you work with and the industries that you want to work in, it really gives that kind of freedom.

And I really do think there’s just a huge demand still for data and technology skills in Scotland, the UK and beyond. And I really can’t see there being a shortage of things to do or places that data practitioners can add value. So yeah, I would say if people are, thinking about it, it’s worth exploring a little bit to see what opportunities might be out there.

It’s certainly never going to be anything but interesting. 

Adam Sroka: This is related but you mentioned it earlier, like reaching out to peers of CDOs and creating peer networks of, other directors and another like part of the story. How important is net that network as a a tech data professional? 

Elizabeth Hollinger: Yeah, I actually think it’s, it is hugely important.

And I would go back to what I said earlier that I think even more so if you’re working in industry, it’s really important to be up to date on what is happening across industry. And that’s from technology providers themselves. What are Microsoft doing? Google, Databricks, and tech, but also what are your peers thinking about, what’s their strategy, what skillsets they seeing their pain points?

And I’ve always found that the data community. In particular, both in Scotland and UK wide have been really open to having really honest and transparent conversations. People are really willing to share the things that they’re struggling with, to offer some advice on how they may have approached a particular problem that you’re facing.

So I’ve always found it to be a really collaborative environment. And I think it is really important that as a data and tech practitioner, You are well looped into those networks with your peers because things move so, so quickly. And I think actually, the thing that made me almost appreciate this, I think, fully, was taking a year out on maternity leave both times.

It was really interesting because when I came back to work, people were talking about different things than when I’d left. And that was only a year. And I was just really interested in that. So when I finished up on my first maternity leave. I came back and that was the January of 2022. And everybody at that point in time was talking about data mesh.

I was, what’s data mesh? I hadn’t heard of it. It wasn’t a thing in 2020 before I finished up. What’s happened now? And so it was really interesting though, because almost every conversation I joined, somebody brought it up in any of the roundtables. So I was like what’s data mesh?

Seems to have died down again a couple of years later, but it was very much a thing. Because I remember not understanding what it was and having to go out and find out what had been happening in the last year. And then after this maternity leave coming back in 2024 after taking in, everyone’s talking about chat.

Bt how do we build an ai? Build an ai, how do we do it? What do we, yeah. It’s just, but it’s so mainstream there. I think because of the coverage we’ve had in the media, because of the accessibility of chat, bt because if you want to Google now and Google anything, it brings you up, AI generated results.

So it’s really pushed it into the mainstream. But actually back in 2020, that wasn’t really the case. I think that’s what’s been really interesting, like that one year, things just switch and change. And it was really obvious for me, just as I say, taking those two years out and coming back to a new conversation.

Adam Sroka: It’s funny, the stuff that sticks and in hindsight, thinking should I jumped on that hype train? Should I not? What did I back? But I think, yeah, we’ve seen a fundamental change in the way business is working. And even the other day, I was looking at, Databricks Genie. Have you seen that?

And like the plain text BI report generator in Databricks, it’s probably very expensive, but it’s amazing. Like it’s going to change the way things are done, if you’re on the right platforms. So conscious of time, any predictions or hot takes on technology and the direction of travel over the next few years?

Elizabeth Hollinger: Yeah, I’m just, I think the hot topic on any data forum at the moment is AI, isn’t it? It’s all the chat GBT, how do we implement it, what do we do with it? I think there’s a lot of scaremongering that exists in terms of reporting on some of it and, the robots are coming to take our jobs. I don’t think that will happen.

I think what will happen is that we will automate some of the things that we do just now by using computers and technology, which will enable us to do different things. Because as humans we need purpose, so I just don’t believe that in the near future that we’re going to automate so many things that actually we won’t ever have to work again.

I just, I don’t believe that will be the case. I think what we will be able to do is to use some of the tools in Tech Tower Advantage to make our jobs a bit easier and to make, and to help us to make better decisions. I think a kind of really good example of this is how we’re starting to use AI, machine learning, and medical imaging.

So now we can identify cancer, before it’s even cancer, by something that the human eye can’t see, but that an AI agent can identify. Identify, flag to a doctor, See, this person is at risk and therefore you might take some preventative action, you might start some treatment early to make sure that doesn’t develop into a cancer.

And that’s a really good example of absolutely we are using the tools and technology. This agent is doing something that we do not fully understand. But they are doing something and they see something that if we take action on it, there is then benefit for us. And I think we’ll start to see more and more of that enhancing and augmenting our human decision making by better utilizing AI data and technology that’s available.

Adam Sroka: Okay. So one thing I like to ask everybody before they leave, if someone in the audience is really inspired, wants to follow your journey, or if you have anything to plug or promote, where should we send them? 

Elizabeth Hollinger: Yeah, so always happy to make new connections in the data and technology world. And please just connect with me on LinkedIn.

I will be better at posting more often now that I am back into the working world. I’ve had a bit of a hiatus over the last year or so. But yeah, always happy to meet new connections. And yeah, I typically tend to attend data and tech events in Scotland and some UK wide ones. So yeah, if you ever do see me, please pop over and say hello.

Always good to make new connections. 

Adam Sroka: It’s been a pleasure as always. Thank you very much for coming on the show. 

Elizabeth Hollinger: Super. Thanks so much, Adam. It’s great to catch up. 

Outro: Thank you for tuning in to the Hypercube podcast. If you enjoyed today’s episode and want to dive deeper into the AI in the energy sector, be sure to subscribe.

So you never miss an episode. We’re always eager to hear from our listeners. So if you have any questions, any topics you’d like us to cover, or feedback, you can reach out to us on LinkedIn or check out our website at wearehypercube. com. And remember, the energy landscape is evolving fast. Stay ahead of the curve for the latest strategies and solutions right here on the Hypercube podcast.

See you next time.