Season 1 Episode 8: Duncan Bain, SAS, on the Future of Forecasting in the Energy Sector

This week, Duncan Bain, Senior System Engineer at SAS, sits down with the Hypercube podcast to talk about forecasting.

Forecasting is the cornerstone of an efficient and reliable energy network, and it is a staple of the sector. Duncan gives his take on how it has evolved over his 15 years in the industry and the impact of new technologies, from distributed assets to AI. Alongside the technical aspect, he also digs into the human elements that can be a barrier to the adoption of digital technologies in the energy sector and how to overcome them.

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S1E08 Duncan Bain

In this episode, we covered:

  • The need for forecasting in the energy sector
  • How new trends and technologies are influencing forecasting techniques
  • The value of industry-specific expertise
  • The problem of people and overcoming human barriers to AI success
  • Navigating AI hype cycles to ensure technologies deliver maximum impact

The weekly Hypercube podcast sits down with leaders in the energy and utilities sectors to explore how data analytics can help businesses make smarter decisions and accelerate business growth.

CHAPTER MARKERS

[0:44] Duncan gives an overview of how he fell into the industry before touching on what is involved in his current role.

[4:00] Host Adam asks Duncan about the importance of forecasting in the energy industry, particularly around new technologies.

[6:51] Duncan explains the need for different forecasting techniques to deal with the increasing use of distributed energy resources.

[8:36] Adam talks about a recent Hackathon run by Hypercube and his takeaways from the event.

[9:33] Duncan emphasises the need for critical thinking to enable teams to create features that account for differences in behaviour between different subsegments.

[11:07] Duncan shares his predictions for the energy sector, particularly forecasting tools to deal with increasing demand for flexibility services.

[15:04] Duncan and Adam discuss the benefits of working with industry-specific experts and the value of working with a partner network.

[18:52] Duncan talks about the human challenge of helping customers embrace the changes they need to achieve their goals.

[22:05] Duncan highlights the importance of aligning AI development with business objectives rather than getting caught up with hype cycles.

[24:38] Duncan shares where you can go to find out more about his work and SAS, including details of an upcoming hackathon in Las Vegas.

TRANSCRIPT

Hypercube Podcast Transcript

Title: Duncan Bain, SAS, on the Future of Forecasting in the Energy Sector

Host: Adam Sroka
Guest: Duncan Bain

Intro: Welcome to the Hypercube podcast, where we explore how companies in the energy and utility sector leverage data analytics to make smarter decisions and accelerate business growth. I’m Adam Sroka, founder of Hypercube, a strategic consultancy that supports asset owner-operators, traders, route-to-market providers, and energy services companies to unlock the power of data.

If you’re interested in hearing real-world examples of how data and AI are advancing the energy sector, this is the show for you. 

Adam Sroka: Welcome back to the Hypercube podcast. Today, I am joined by Duncan Bain. We’ve both worked in the data and AI space for a while, and you’ve had a really interesting journey, actually to where you are now, do you want to just expand on that a little bit? 

Duncan Bain: Like a lot of people who’ve been in the industry for a while I kind of fell into it My journey with day analysts has really started quite a long time ago. I worked in pensions and investments Yeah. I just think kind of kind of better ways of doing things.

So why don’t we do this stuff? I might be interested, you know, what are the numbers about this? Could I really, before anyone thought of the phrase data scientist or anything like that? So you, I’ve been doing data things for a long time. So the way back in the pensions investments world, and I’m also a big fan of automation as part of that.

So you are in one previous role, a colleague of mine, and I inherited a very large report that we had to do. And many of that would take an entire day. Through the power of automation, we were able to knock off early and go to the pond while that did itself. And then we’d come back and look at all the very hard work we’ve done to produce this report.

Unfortunately, you know, success, you know, it just breeds more work. So, you know, I’m really sure I don’t live in victory, but after a long period of investments, I moved into the energy sector. I worked at Storage Power and as retail for over a decade, uh, where I finished my time as head of data and insight.

And then what’s almost 10 years ago, I jumped the fence or tunnelled under it to become. Okay. 

Adam Sroka: So let’s change gears a little bit into what does a role the vendor side looks like compared to the customer side of the energy sector. And what’s the kind of day-to-day or week-to-week of your role at SAS these days?

Duncan Bain: So SAS is a really big company. We were founded in 1976. So SAS has been doing, you know, analytics, machine learning, AI the marketing guys will say that we invented it. I’m not quite sure that’s 100 percent true. But, you know, we’ve been doing it for a really long time. I think it was either our second or third customer was in the energy industry.

And so we’ve maintained those links for a really long time. Where I sit in the business, I think anywhere else you would think of it as technical pre-sales. Right. But it’s a bit of a broader role than that. So that doesn’t quite fit neatly into that slot. So SaaS is big, it’s a privately owned company.

So, you know, we don’t have the same corporate structures as you might if you were publicly traded. So when I’m in customer advisory, my main role is to make sure that we understand what the actual problem is that we’re trying to solve for a customer. And then make sure that they get the right things from us to solve that problem.

And you’ll probably realize that sometimes it’s easy for a boss to come to you and say, here’s my problem. But then what they’re really describing is the symptom of the problem, not the actual problem itself. 

Adam Sroka:  Yeah. Okay. 

Duncan Bain: And so a lot of the work we do around discovery is to figure out what’s the real challenge that underlies the thing that you’re seeing.

Now, they might be the same thing, but often they’re not. And then to make sure that you get the right software, the right support to be able to resolve that problem. And ultimately my goal is to make sure that people can be. More decisions faster and more accurately, because that’s where the rubber really meets the road.

Adam Sroka: Yep. Okay.

Duncan Bain: I mean, it’s great to have a really very clever model, but if it just stays on somebody’s desktop and never appears in any kind of decision. It’s not really added any value. 

Adam Sroka: Yeah, of course. Yeah, yeah. 

Duncan Bain: That’s where the value is in terms of the kind of services we deliver. It’s in making sure that you as a customer can make good decisions, make them faster and make them more accurately.

Adam Sroka:  So I guess you’ve gone with 50 years is a long time, isn’t it? Like, I guess you must have teams and individuals, even that, you know, really like as a company, I mean, you know, really, really well that you’re supporting them as they navigate the changing energy environment, the energy mix and things like that. If you had any kind of firsthand experience of people coming to you with like big, scary, new problems and like batteries or hydrogen and things like that and say, Oh, how do we forecast?

Duncan Bain: Right. I mean, so if we think about forecasting for a little bit, so forecasting is something we’ve done for a really long time.

You know, everybody, everybody likes forecasting. It’s quite conceptual. It’s quite easy to get. So you go, well, here are the things that I know from the past. You know, what does that mean in the future? We do that across all sorts of industries, right? So from retail to energy. As with everything else, it’s got to be a little bit different.

I can say that sometimes the scale or the scope of those forecasts is very different. So we have customers who are network operators who are doing 30-year forecasts because They’re trying to plan in advance to, you know, for their network infrastructure, because that’s a, you know, replacing that storefront building, that’s a non-trivial task.

And it can only happen in real time, and there’s no, very few ways you can make it happen faster.  Because, you know, you’ve got to take care of people’s safety and make sure that everything’s, you know, plumbed in properly. So we’ve got people doing things over 40 years, and so the majority of our customers are doing things in a much shorter timeframe.

You’re looking at entities who are trading energy on a day-to-day basis. Here in the UK, the smallest tradable unit is half an hour. In Europe, it’s 15 minutes. And then we’ve got other customers across the world who trade in an even smaller unit. So that’s not a five-minute segment. So you need to know what’s happening right now and be able to figure out what’s going to happen next very, very quickly. 

In order to be able to trade on that energy, because, you know, a lot of the people listening to this will be energy experts, but for those of you that aren’t, you can’t have more energy in the system than it can take, right? You can’t put a quarter in a bike park, that’s just not going to work. So you’ve got to make sure that the amount of energy being generated and supplied into the network is more or less equal to the amount that’s being taken out. 

Balancing that up to a five-minute level is quite challenging, especially as we see inertia coming out of the system where we replace lots of big spinning disks with turbines or solar or any of these other renewables that don’t have that kind of, you know, that mechanical component. 

Adam Sroka:  One of the things I always find that customers struggle with when they start to really want to get into forecasting is they, the typical journey is like, we do this stuff in Excel and we get some answers. We want to take the next step out. And then they just smush into like real-time or new real-time time series forecasting. And it’s just really hard. They don’t, they don’t know how to take that next step. Is that something that you see in SAS and I’m guessing you’ve got mature kind of processes and architectures to help with?

Duncan Bain: Yeah, I mean, we’re also seeing a need to forecast,  you know, something that we’ve never ever really thought about kind of five, six, seven years ago, which is we’ve got a whole lot of installed capacity. Well, the amount of installed capacity, if it’s not the amount of energy you get out of it. Yeah. Like, that’s just how much you could get out of it if it was 100%, yeah?

So, for example, you know, solar PV  Right. Yeah. So you need to take care of that. And one of the things that we’ve seen, you know, significant growth is the requirement to forecast the outside and from those distributed assets. So not just the demand side of how much of my customer is going to consume, right?

Because I need to plan in advance so I can create a hedging strategy that works for me and for them. But also what’s the impact of these other types of generation on that forecast? And the techniques that you use to do those are very different. So, you know, the, you know, classic time series, you know, it is a well-worn path.

All right, and you know, those classical statistical methods still almost always outperform clever new stuff. All right. Now that’s not to say that you can’t achieve the same results with a neural network, but it will cost you an awful lot more to do that. All right. So, you know, if you look at the cost per forecast basis, right, you know, those kinds of traditional methods, you know, are well understood, perform the best to do that.

But if you’re looking at those kinds of. You know, distributed engine resources, then you are looking at things like convolutional neural networks, and long short-term memory. You know, those are the techniques that are making the difference in that kind of field. Although it all kind of falls under that banner of forecasting,  you’re trying to forecast two kind of similar but very fundamentally very different things.

And so, you know, you’re picking the right tools for the job. You know, it’s definitely, you know, key to doing it well. 

Adam Sroka: That’s a really valid point that very raw firsthand experience of we ran a hackathon, 24-hour energy forecasting hackathon last week and some really good teams and really good performance and mixtures of approaches like complicated mixtures of models, model stacking, some neural network stuff.

And it was a logistic regression that won it. A very well-tuned logistic regression. With the right parameters, the right assumptions, and a few really clever transformations on the dataset smashed literally by like a 10, 20 percent accuracy gap between first and second place. It was, it was interesting to see.

And it just goes to show that focusing on those sort of fundamental approaches is still, this is going to be a game that’s not going to change anytime soon, I don’t think. You’ve got teams out there that have been doing it for so long that, that know this, it’s so well understood that, yeah, in this world, the, the kind of good fundamentals hold true.

Duncan Bain: Yeah, and certainly in that space, like, really good critical thinking about,  What’s driving that? So the enabling to create, you know, features that perform better to identify things like sub-segmentation, where not everyone’s going to perform the same. So, you know, we’ve got really good evidence of this from COVID, right?

So the assumption is that everyone was working from home during COVID, right? And as an energy provider, we knew that wasn’t true, right? Because, you know, we know a lot of people were, right? So, you know, the consulting moved around a lot.  Right. Well, there’s still a significant number of people who are in roles that need to be in a particular place.

It’s not their home to be able to do those. So, you know, if you work in manufacturing or in retail, or, you know, you’re, you were working at one of the key worker schools, right? You couldn’t do that from your home location. You were still going somewhere and doing, right. So I think the tendency for, for kind of people in the tech space to think that everybody does the same thing as them.

Adam Sroka:  Yeah. Yeah. 

Duncan Bain: You know, my old team will be completely bored of hearing me say, you are not the target demographic. Right. Well, I think it’s so important that we remember that because we are not the target demographic. You know, except sometimes we are, but there are a lot of people whose lives are fundamentally organized in a very different way to ours.

And we need to be able to support them as well. 

Adam Sroka: Okay. I wanted to ask a little bit about what were your sort of predictions for how data science and data and AI is changing. What are your sort of predictions on what’s going to be interesting in a few years to come and how that play a big role specifically in the energy sector?

Duncan Bain: I’m going to settle on a couple of things. I’m going to say forecasting again. And not just because we just talked about it, but because You know, we’ll start to see a lot more of requirement for flexibility services on the forecasting is going to be a key part of that. Especially if you’re an aggregator, right?

And you want to take people’s big batteries that they’ve got parts of their driveways and do something with those. There’s an implication in terms of network infrastructure. So, you know, we need to do, yeah, we’ll work on things like extra grid. Yeah. Well, in order to support that, you know, we’re going to need to be able to say, well, how much of that is going to be available?

So again, that we can plan appropriately, you know, the companies that are involved with space, you can trade efficiently, you know, they can create hedging strategies that work, you know, both from a customer perspective, but also from a corporate perspective, right? So if we’re creating a hedging strategy, that’s amazing for customers, what means your company folds, you know, two weeks later.

So that’s partly, you know, all those things did balance, right? So I think there’s going to be increasing interest in forecasts of new types. So I think that demand forecast is very much going to still be good quality linear regression, you know, good high-quality feature engineering. And then we’ll start to see different approaches taken to things like how much are we going to get from solar, how much are we going to get from wind, how much is going to be available potentially in distributed storage. We’re not quite there yet because that doesn’t exist. Yep. But, you know, we have to start planning in advance for that because there’s a bit of a chicken and egg saying, where were you? And everyone’s going to hover around the edge of that. We don’t want to get electric cars because the infrastructure is not there.

And we can’t afford to invest in the infrastructure if the cars aren’t there. Yeah. Yeah. And again, I think we would benefit as an industry from a little bit more certainty from the government around things like, What’s the strategy going to be around hydrogen? So the Red Car Hydrogen Trial is not going ahead.

There’s a different trial going ahead for that. It’s going to be a big part of the mix. But is it going to be used significantly in a domestic setting? Or is it just going to be industrial, hard-to-obey industries, that kind of thing? And if so, how are we going to move it around? How are we going to store it? You know, are we going to use hydrogen to store the energy that’s generated but not needed right now? Because we can, you know, it’s better at storing that for longer than putting it in a battery.  Are we suddenly going to get a big break in battery technology that means that we can, you know, store more stuff? And we don’t see the decay in the batteries as fast. There’s a lot of inventions still to be done. 

Adam Sroka: Yeah, okay. 

Duncan Bain: Fundamentally, you know, there’s going to be a huge demand for forecasting different types of resources, which wasn’t there kind of five years ago. 

Adam Sroka: And do you get to see a lot of the differences being like a global business? I guess, do you get to see a bit more of that now that you’re in a UK-centric one?

Duncan Bain: Absolutely. So, you know, we work quite closely with our colleagues, you know, all around the world. So. We’ve got, you know, business experts doing all sorts of things for us. You know, we can’t double up with them, necessarily. Or we’d be very, very, very big then. 

So the bulk of our D footprint are in the US, but we’ve got also R& D in the UK, here in Glasgow. We’ve got different business experts on different solutions we have, you know, all across the world. So there’ll be somebody somewhere who’s encountered a problem before. And I think that one of the great things about working for SAS is that there is definitely somebody somewhere who knows the answer to any question you’ve got.

They might be half a day away in terms of, you know, where they’re at. But, you know, everyone’s brilliant at helping out, making sure that, you know, ultimately, you know, what we deliver is a great outcome for the customer. 

Adam Sroka: And does that, from the customer’s perspective, like pick up the phone, they say, right, we need some help with some stuff and they wheel out Duncan. Does that, for the customer, having someone that’s lived in their world, does that make a significant difference? Does that have the that kind of impact that you speak their language, you understand their world? You’re not just a grad with those super bright. You also have a load of industry experience. And is that core to like SAS’s approach to solving problems?

Duncan Bain: I would say that’s definitely one of the things that marks us out in the marketplace is that we do have a lot of people who’ve come to the kind of vendor side of it as kind of a second or third career. And so, you know, people have been deeply involved in some of the energy industry, in my case, or, you know, people who’ve worked, you know, inside government, um, in various different parts of the world, right.  Because it’s like they say, you know, Britain and America, two countries separated by the same language. Right. But it’s, it’s the same for kind of any business sectors that you talk about things in a way that you’re using the same words, but you don’t mean the same thing as if you were talking about, you know, kind of retail or manufacturing or something like that.

So I would definitely say that’s one of the things that really what says we do have a huge amount of industry expertise, but I think we also know where our gaps are.

Adam Sroka:  Yeah. Okay. 

Duncan Bain: There are some things that we say, you know, we don’t know enough about this. Thank you so much. Right. In which case, you know, we’ll find a pilot to work with who does.

Adam Sroka: Which is great to hear, right? Because so many people would just be like, No, yeah, we know that too. And we’ll figure it out as we go. Maybe spend more money than you’d hoped. 

So that was a super-leading question because I bet my whole business on that. That’s totally what I wanted to do with Hypercube Try and do exactly that, solve problems, but just for the sector that we know, and just say, right, we are also domain expertise in this space. And we were having a conversation recently with someone who said, like, they got into big consultancies and they were doing, they’re great people and the teams are very talented and they get loads of work done. But there’s just, they’ve never done like volume forecasting. So it’s so hard to just. Go through the 101 of that domain and things that I can imagine for a company like SAS, but the resources you’ve got, being able to deploy a team of experts actually can really make customers feel like safe and that they get it. And that you’ve been through the code changes and the data set weirdnesses that they’ve seen as well. 

Duncan Bain: Yeah, definitely. I don’t think there’s a problem that somebody hasn’t seen somewhere. Um, so, you know, that’s kind of a really good thing we can bring to the table, but we’re not the biggest company in the world, right?

Yeah, well, I think we do a pretty good job, but, you know, we do also very much rely on our partner network, right? So, you know, there are partners we’ve worked with for, you know, either a really long time or just for a short time,  and sometimes those partners are building out solutions using our software,  but it’s configured a package in a way that works for their customers.

And that’s true, again, so, you know, kind of regardless of what industry you’re in, you know, they’re all super bright people, right? So, you know, sometimes it’s just about us helping them get the most out of the platform and they’ll come up with a solution for that kind of joint customer together. So there’s a lot of different ways of working, especially if you look at sort of the IoT space, where even if you’re an expert in deploying IoT solutions, Every customer is very different. 

Everyone’s got a different plan in different places. They’ve got different existing kit. I don’t think there’s a single IoT solution that you wouldn’t think of in some way as being bespoke. Because you might be trying to solve the same problem and the facts on the ground are almost always very different.

Whereas like it’s slightly easier with the kind of classic demand forecasting because that’s been around long enough that, yeah, everybody knows more or less what the objective is and what data they need to put into that, not to say it can’t be improved, but it’s not as bespoke,  for example, as some kind of IoT solution for, say, plan optimization. 

Adam Sroka: From your experience, both at SAS and before, what sort of hurdles do you see then in the energy sector, both legacy and new, what hurdles do you see to companies being able to really, truly adopt like a digital-first strategy and make use of kind of technologies coming down the pipe and expand a little bit on like, does SAS leading in any direction or trying to empower any one flavour of that? 

Duncan Bain: Our objective is to empower all our customers to be the best version of themselves that they can be. So it’s not really for us to dictate what they should do. I definitely think that the challenges are though,  and first of all, we were talking about people, right? So the hardest part of any project is the people part.

Adam Sroka: Always. 

Duncan Bain: You’re asking people to stop doing what they’re up to and do something else. And, you know, everyone is naturally suspicious of new things. That’s how we’re so successful as a species, right? You know. Not just punking things, but you go, what if this is okay? Like, thinking about it first, it’s been a bit cautious, right?

So, you know, it served us very well in our evolutionary past.  So, you know, I think we’re slow to adapt to change, not because people don’t want to change. Right, but because, you know, it turns out the way that they’re doing something there might work quite well. 

Adam Sroka:  Mm hmm. Mm hmm. 

Duncan Bain: Right? Or it really should be perceived to work quite well, right? Is that change going to lead to a quality of life improvement for them or not? And it’s hard to get through any kind of technical change if there’s no associated quality of life change. So if something is better for the business, but less good for the employees in terms of how they perceive that process working.

That’s a hard change to get through. Whereas if it’s something that benefits them, it’s much easier to get that change adopted. Because you can deploy a new system where nobody uses it and just, you know, goes back to, well, I don’t really understand how I can get a change made. I’m just going to extract all the data, do it in Excel and create my report and send it to everybody. That’s how that happens.  

Adam Sroka:  There’s like an asymmetry to, I think a solution has to be like orders of magnitude better to overcome even the slightest quality of life barrier to adoption, like even if it’s one more click, I think the tool has to be orders of magnitude better for people to naturally want to adopt it because people do get so ingrained in the way they work. And I always found that unless you actually let them design it with you, you’re almost destined to fail. It becomes so hard to, to hand something over as finished. If the end-user hasn’t had some say in how that’s going to look and feel for them in their day-to-day.  

Duncan Bain: And there’s also a role for education in that. So, you know, sometimes it might not be obvious what that quality of life improvement is. 

Adam Sroka:  Yeah. Okay.

Duncan Bain: If you just go, here’s a new thing and it might be great,  but if nobody knows how to operate it, they’re just going to go back to what they know.  And so, you know, one of the services that we provide at SAS is that like ongoing learning and training so that you can, A, learn how to operate the, you know, the tools in your, in your environment, you’d see your best benefit, but also to help you think about new things that you should do. Yeah, okay. 

Adam Sroka:  Like spot new opportunities and ways to leverage the product to do more.

Duncan Bain: Everyone wants to get a bit more juice out of Eleven, don’t they? Well, you can’t be just left to them to try and figure that out themselves. You know, I think we’ve got an obligation to help people get the most out of those investments. 

Adam Sroka: And I guess with such a history at SAS, like you’ve been through the mill a bit with it. You’ve seen what’s worked for other customers. You’ve got that organizational institutional experience of actually this might be UNS BEST there, which a lot of younger sort of startups might not have the benefit of. 

Duncan Bain: Yeah, I mean, this is definitely a benefit to having been around the block a few times. Yeah, too, right? Yeah, I’ve been to a few of the different hype cycles as well. So, you know, it’s definitely, you know, I think everybody accepts that we’re in another one right now, right? 

Adam Sroka: Are we? 

Duncan Bain: Well, probably. Well, you know, I remember going to do the first kind of big AI hype cycle where It was neural networks for everything.  And people spent, you know, huge amounts of money and time and then realized that, well, you know, they could have got that result weeks earlier if they’d have just gone, oh, we’ll just use this regression. Yep. I think assessing what the impact’s going to be before you decide to go down that route is worth doing.

So, you know, it’s very tempting to, if you’re at the academic scale or end of the spectrum, to go, well, now I want a model that’s, you know, Just the best one, right? Well, then if you’re only operating in units of a whole customer, how much does a 0. 0001 percent improvement actually make a physical difference to the business that you’re in? 

Has it been worth spending all that effort on it when you could have done to solve a different problem? Yeah. So, you know, and again, that’s other people’s challenge in that, you know, people want to be able to show that they’ve worked on something that’s really cottage and it’s the best one, but maybe that’s, it doesn’t, you know, a hundred percent aligned with the objects of the business.

If it doesn’t deliver a significant change to, you know, are we making decisions any differently? Is the customer impacted in a positive way? Has it made a difference to your colleagues in terms of the work that they’re doing? 

Adam Sroka:  As opposed to CV-driven development and trying to deploy the news tools. 

Duncan Bain: As opposed to bragging rights. 

Adam Sroka:  Yeah. Yeah. Yeah. 

Duncan Bain: Yeah. You’ve got bragging rights over the best one in the world. That’s cool. Right. You know, from a business perspective, it’s not delivering any additional value. So people’s stuff is definitely. You know, by far and away that the hardest thing to do. So, you know, I think you being able to take a little bit of a breath, especially in the middle of these big hype cycles and say, well, you know, what is it we’re trying to deliver in terms of customer outcome?

That’s quite a skill. And it’s quite difficult, you know, even for a big, kind of a lot of companies like SAS to say, you know, what’s the, you know, we can all read the tea leaves and see what the direction of travel is. But, you know, what does that really mean in terms of delivery for the end customer? 

Because not everyone’s trying to do the same stuff at the same rate, or even in the same way. So trying to help people navigate through some of that hype is definitely quite challenging as well. Because as you’ll know, there’s a lot of friends being called every single day saying, How can I, I want to think that it’s made with generative AI. And you’re like, what for? Well, because I can have one. 

Adam Sroka: We need an AI as the current flavour of the moment. Yeah. The less said about that, the better.  Okay. Look, it’s been really good to talk, just conscious of time. Is there anything you would like to promote or point anyone out? If people are keen to learn more about you or SaS, can we send them anywhere?

Duncan Bain:So you can find more about me on LinkedIn. Search, uh, for me, I don’t think I’ll link to the unique, but you know, find me there. Uh, if you want to know more about SAS, you can go to www.sas.com. Let’s go to the website, lots of stuff there. And also the other plug I want to make is for the SAS hackathon.

Okay, so it’s happening a bit later this year. So normally we do it at the start of the year, but 2024 has been moved out a little bit to coincide with our flagship event in Las Vegas this year. All you need to do is bring some people and an idea. 

Adam Sroka:  Nice. 

Duncan Bain: And we will provide you with the full stack of SAF software, including some of the things that aren’t even GA yet, for you to have a play around with.

Adam Sroka: Oh, very cool. 

Duncan Bain: We will support you through that process. So we can’t do any of the work for you, but you’ll get a mentor, or somebody like me who understands how the software works, and you can solve any problems in terms of making sure that your data’s getting in and everything’s lining up right for you.

But we’ve had some fantastic entries, you know, over the last few years. I would highly recommend that to anyone who just has a kind of really out-there idea that You know, they can’t persuade the management team to let them do in-house because they need a different kit, or they just want to try it out and see, see how it works.

All right. So it’s a fantastic competition, and we had some really amazing entries last year in the energy space, but also, you know, right across the piece. 

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

Duncan Bain: Thanks, Adam.  

Outro: And that’s it for this episode of the Hypercube podcast. Thanks for tuning in today.

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It all really helps. See you next time.