2025 Sustainability Data Management Big Bets

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Transcript

Saif Hameed [00:00:00]:

If I was a customer, I would look for a sense of what the vision or plan is. And you don't need this in a huge amount of detail. But I would want to know, does the solution provider intend to create the best database for apparel or for food or for something? Do they intend to be an aggregation layer on top of other databases? Do they intend to build their own? Like, what is the plan here? It is a useful thing for me to know because it will ultimately affect my numbers over the next two or three years. So I would kind of ask this question, what is the plan? What is the strategy? Where are you going with this? I think there's a second piece which is what is the science behind this? What is the scientific rigour? Not just who has verified or signed off on the factors you're using, if you're creating factors or sourcing factors from from elsewhere. I'm Saif Hameed and this is the State of Sustainability podcast. If you like what you hear on our podcast, please join us in Chicago for the State of Sustainability Summit where we discuss all the topics that we cover on our podcast and more in person. The signup link will be in the show notes below.

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Isobel Wild  [00:01:15]:

Effective climate data management is widely understood to be a critical component for organisations aiming to meet their sustainability goals. But as we look ahead, we anticipate two big bets as well as two red herrings in how climate data is managed, shared and secured. So in today's episode, we essentially want to prepare our listeners for what is coming. We'll explore how professionals can streamline their climate data management practises, anticipate upcoming changes, and ultimately stay ahead of the curve. Saif, let's kick off with a bit of an agony aunt session. What are the most common challenges in managing climate data at the moment?

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Saif Hameed [00:01:54]:

Ooh, wow. I think that the challenges now are like the same as the challenges last year. Like I. I don't think the challenges have shifted a whole lot. What has shifted maybe is that there was this time last year a smaller group of companies was facing the brunt of the worst types of challenges. What I mean by that is that we were talking about flag, you know, whatever, two years ago and two years ago a very small set of people was worrying about how to get their data ready for Flag, and a year ago that set of people was actually having to do it. And now this year many more companies are going to be dealing with that and factoring that in. Similarly, two years ago, spend based scope threes certainly were still very much, not just the norm, but even the Ambition, I would say the, the norm was much worse last year.

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Saif Hameed [00:02:48]:

We started to see a lot of shift towards activity weight volume based calculations for Scope three this year I think that's going to be the norm and the expectation. Most companies are out there looking for solution providers right now, if they don't already have one for their environmental accounting. Solution providers could be consultancies, could be software companies like Altruistiq . And I think that they've realised that this expectation of weights, activities, you know, location specific, Scope three flag, if it's relevant for them, should just be requirements in their rfp. And so all of this I think is going to play in the other thing that I'm hearing a lot more about, especially in the consumer packaged goods space and the sort of the consumer space more generally is preparation for all the other regulatory burdens and to some extent opportunities that are coming our way over the next couple of years. So last year everyone was talking about preparing for CSRD this year, this year everyone is talking about like whether it's CSDD or EPR with respect to packaging or European digital passports and like what does that mean and what do I do about that and is it a plus or a minus in my agenda?

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Isobel Wild  [00:04:03]:

So would you say that in general the management practises are the same but perhaps the load and the reporting requirement load is bigger?

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Saif Hameed [00:04:12]:

Sophistication has definitely improved. So I would say that companies are getting better at this. Like for sure, companies that we work with, for example, and that we work with for, let's say a year or two, we have noticed the shift in professionalisation of how sustainability data is approached, whether it's about coordination between IT teams and sustainability or finance teams and sustainability, that sophistication and professionalisation that businesses have brought to other functions and other functional interactions is happening on sustainability. So management practise with respect to data is definitely improved, I would say. And I think that that is going to improve by leaps and bounds this year. And the reason for that is that everyone is doing CSRD this year. And so if you are using solution providers for that, you are ironing out all the kinks because hey, guess what, the solution providers are also new at csrd. Everyone is.

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Saif Hameed [00:05:10]:

And if you're not, then you're probably thinking about what you do in terms of solution providers for next year. But all roads are leading towards more sophistication and more professionalisation.

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Isobel Wild  [00:05:21]:

I want to take us onto our big bet. So for context listeners, I proposed to Saif three big bets. And Saif quickly responded that two of my Biggest bets were in fact red herrings. So we did a last minute change of the structure and we changed it into two big bets and two red herrings. So if we start off with the big bets, big bet number one is that emission factors will become commoditized. Saif, can you explain what this means in a bit more detail?

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Saif Hameed [00:05:52]:

Yes, with great pleasure and even gusto. So if you're doing your emissions calculation internally or if you're working with a solution provider and they are doing your emissions calculation for you, everyone is using some sorts of emissions factors. Now you could be using, let's say one of the large open source databases like an Axiobase. You could be using something like an Ecoinvent. There are many different platforms that aggregate LCAs in, like a CD Schema Pro or something. Whatever it is, you're probably relying on emissions factors that are procured from somewhere. And there's a lot of sort of incestuous relationships between the different databases as well. Some are built on top of others and leverage others and some aggregate many other databases.

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Saif Hameed [00:06:37]:

So they're all kind of, you know, they're all have these complex relationships. Now what has happened over the last like 12 to 18 months is that many of these databases have started to realise that the demand for their offering has radically increased. But they're actually not monetizing much of that because the monetization is being done by the solution providers. Whether you're a consultancy, whether you're a software company, the last two years have generally been good for price premiumization of some sort. Like prices are generally rising in this space. If prices aren't rising, it's because demand is rising and you want to capture a disproportionate share of the demand and you're keeping prices low. But it's a good market for this space. Emission factor providers have generally gone with very low pricing for their product because there wasn't a huge, there wasn't a huge value addition.

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Saif Hameed [00:07:31]:

I would say that they were doing on top. This is my hypothesis. Others may disagree. And their branding was pretty weak. Like it was not visible to the consumer or the third party. Like if you're looking at McDonald's carbon footprint or Mars's carbon footprint, it's very hard to suss out who the emissions factor database provider was. So it's hard to extract margin from that. What's changed is that a lot of those database providers have now started to try and capture this.

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Saif Hameed [00:08:00]:

And I expect that this year we'll see prices of emissions factors go up for a lot of the larger database providers. I think that that's a miscalculation because I think that nothing has fundamentally changed in the market in terms of the brand capture potential for the emissions factor databases. It is still going to be the case next year that if you look at McDonald's footprint or Mars footprint, you won't know who the emissions factor database provider is. And frankly, if I tell you that the emissions factor for a know kilo of chicken is X and I have a methodology and some rigour behind it, you're probably not going to care or wonder where that emissions factor came from in terms of who did the math to bring those things together. And so what I think is that I think as these prices go up, I actually think we're going to see an explosion of the open source emissions factors. I think a lot of solution providers are going to create their own databases and I think that this whole space space actually gets commoditized as a kind of almost like a whiplash effect of a few of the databases ramping up their prices and margins. Like sometimes when you ramp up the price and create a lot of margin, you just attract a lot of competition because you've suddenly created a pie where there was no pie before. If it cost you nothing to secure emissions factor databases, why would you create your own? Now that it costs a lot, you're suddenly going to go out and look for other options.

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Isobel Wild  [00:09:28]:

I guess it's also going to attract a lot more scrutiny on the quality of the emission factors as well, and it will cause people to go to one emission factor database versus the other. But I'd be interested to hear your thoughts on what the actual take home is for sustainability professionals. Does this mean that they should perhaps consider an increase in their budget to account for these? Is it going to price out some people? And if so, like, will the open source be accurate enough? What should we anticipate?

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Saif Hameed [00:09:58]:

Yeah, I mean, I think it is worth being on top of this. If this is part of the domain that you're responsible for. If you're the person who's responsible for your environmental data management calculations, et cetera, then it's worth having a perspective. The reason I say that is because let's say that you're using a cheapest chip solution provider that's coming in at a few thousand pounds a year, a few thousand dollars a year for your solution. That is probably the minimum threshold at which most emissions factor databases are going to start monetizing on a per customer basis, which means that you might actually see the price of that solution double or triple as the solution provider tries to pass that price on to you. So either that's going to happen or the solution provider is going to try and create their own database, which might mean that you take a step down or even a step up in terms of Ricker and quality, but you're going to be shifting the underlying coefficients that are being used in your calculation because that solution provider is going to be trying to look for alternatives, whether it's in house or external. So depending on how important that is to you. And there are some companies to whom this is very important.

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Saif Hameed [00:11:04]:

There are some companies today that are very prescriptive in which emissions factor databases they want to use. Like some really want to use the World Food LCA database and Ecoinvent for example. Some will have a particular view on a different database. So I think that it is worth being on top of this trend. I don't think it's going to be a massive game changer for the end customer for the business, but it's going to create a lot of waves in the solution provider landscape and some of that might ripple through to customers as well.

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Isobel Wild  [00:11:34]:

And what are the considerations to keep in mind when looking at solution providers emission factors database? Is it simply just looking at the number that they've got, the size of the emission factors, or is it looking at the quality and the methodology? And if so, how do you assess the quality of an emissions factor?

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Saif Hameed [00:11:53]:

Yeah, so I think that if I was a customer I would, I would look for a sense of what the vision or plan is and you don't need this in a huge amount of detail. But I would want to know, does the solution provider intend to create the best database for, for apparel or for food or for something? Do they intend to be an aggregation layer on top of other databases? Do they intend to build their own? Like what is the plan here? It is a useful thing for me to know because it will ultimately affect my numbers over the next two or three years. So I would kind of ask this question, what is the plan? What is the strategy? Where are you going with this? I think there's a second piece which is what is the science behind this? What is the scientific rigour? Not just who has verified or signed off on the factors you're using, if you're creating factors or sourcing factors from elsewhere, but also just what is almost the scientific ideology if that's not a tautology, what is the logic that you're using to say that an emissions factor is a good one? Or a bad one. How do you differentiate? How do you set the bar in quality? How do I inspect them? How do I understand what went into them? Those things are going to be important. And then there's another piece. And I think that one could talk about breadth, which is, how many factors do you have? Do you have 100,000 factors? Do you have 200? Do you have 300? It's kind of moot in a way because you could have a million factors, but those million factors could all be irrelevant for my business. And so I actually think that what's more interesting is density. So if I am a massive dairy business or a chicken business or a apparel business, do you have great emissions factor density for what I need? Because I don't really care about the other stuff.

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Saif Hameed [00:13:41]:

I don't care if I'm a, if I'm a, you know, if I'm a burger business. I don't care if you have the world's best coverage for office stationery. That's not going to be something that moves the needle on my business. I'm going to care whether you have more than one factor for American beef, which is a rarity right now. So I would look for these as the questions I'd ask.

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Isobel Wild  [00:14:00]:

Big bet number two, data security will create data silos. So as sustainability data becomes more accurate, data security as a requirement is increasingly becoming a priority. Saif, what will the prioritisation of data security mean for data sharing?

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Saif Hameed [00:14:19]:

So there's like a massive interesting development here which is emissions data is becoming better. And that's to an extent, that's what we've been talking about for this episode so far, basically, which is in one way or another, for one reason or another, emissions data is just becoming better quality globally. And that's a really good thing. As emissions data becomes better quality, it says more about your business because it captures more specific activities within your business, more specific qualitative aspects of where you buy stuff from, how you put stuff together, more accurate environmental coefficients to represent actually doing just becomes a lot better. As it becomes better, anyone else can infer more about your business from it and its value, whether it's in kind of risk mitigation, like you don't want that number going public willy nilly, or from a competitive standpoint, like its value becomes meaningful. And often this emissions data are not often, but progressively this emissions data is going to be enriched with qualitative aspects or metadata to tell the user of this data point what the data point is. As in it's not just going to be enough for me to say, hey, the emissions of the packaging material that I'm selling you is 1.5 kilos per unit. You're going to be like, well, okay, great.

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Saif Hameed [00:15:39]:

Is it plastic? If it's plastic, is it ldpe? If it's LDP plastic, where did the raw materials come from? What spec is it? What share? If this is recycled content, there's going to be a lot of this metadata associated with it as well. And that data pack or file is going to be important, sensitive IP effectively, right? Or something which has IP like characteristics. What that means is that more and more companies that are sharing this data at this level of granularity and quality are going to want to protect how it's used, how it's shared, and maybe even how it's monetized. Frankly, like, it is very possible that some of the larger providers of this data in the corporate world are going to want to monetize this data because it also pays for the cost of gathering and putting it together. There are at least two or three companies that I know, for example, that $100billion of revenue, plus or minus, and that supply pretty much every food company in the world, which means that they're sitting on tens of thousands of emissions factors that they're going to need to be supplying. Why should they not monetize that? If I'm going to pay an eco invent or know another emissions factor database for an equivalent volume? So what I think is going to happen, my bad, is that actually we're going to see progressively these data silos, these emissions factor silos come into creation where a lot of these companies, particularly the larger suppliers, are going to say, look, we have a database of our emissions factors, our PCFs effectively. And we have put this together either with the help of a third party, you know, like an Altruistiq , or internally, through our own efforts, we have this, effectively this silo and we are going to share this data with our customers or stakeholders on a case by case basis based on request. And that allows us to control the security of this data.

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Saif Hameed [00:17:25]:

And I think that we're going to see this shift towards that data being shared in this secure way rather than a lot of the frankly haphazard way in which this data is being shared historically. And also rather than any of these kind of single platform aggregation points where we expect all the emissions factors in the world from every company in the world to end up in one place, I think that just is dead in the water. And we're going to see, especially for these Large suppliers, these kind of emissions factor silos be set up. And I think what's going to be interesting to watch is to what extent they monetize them.

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Isobel Wild  [00:17:59]:

I think the first question I have is what is sharing data securely? What does that mean? What does that look like? Can you describe it?

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Saif Hameed [00:18:07]:

Yeah, I mean there's a lot that goes into this, right? I mean, you almost define it by what is insecure data sharing rather than what is secure data sharing. So insecure is. I'm kind of sending this across by email. I have no idea where the email is going to get forwarded or not. There's maybe a lot of interactions also embedded within that. What we would see as secure data sharing is that this data is kind of travelling via API. It's a controlled environment, it's going from one system to another system. There are role based access controls on the side of who can kind of share it.

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Saif Hameed [00:18:43]:

So if you get asked, Izzy, for our emissions factors as Altruistiq , no discredit to you, but you shouldn't be able to just share that unilaterally. It should be someone who actually has controls to do that in our organisation that should be able to share that. With approval requests should come, the request should be logged, there should be some sort of an audit log as well of who did we share this data with and when. The process to generate that data should be secure as well, so that no one can influence with it or tamper it. On our side, it should be shared in a way that it can't be necessarily edited on the other side as well, or altered on the other side. I mean, just off the cuff. And Toby, our VP data and platform will have other views on this as well. But just off the cuff, coming almost from a first principles perspective, these are the things that I would think about.

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Saif Hameed [00:19:32]:

We talk a lot about the PACT API, which is obviously one good way to share this securely, but that's really a starting point, I would say.

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Isobel Wild  [00:19:39]:

And on the monetization point, what does this mean for brands? Spoken also about the increasing costs of EF's. Like it sounds like the whole cost of data management is increasing a lot. What considerations should you be making now to stay ahead of. Ahead of these potential cost increases?

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Saif Hameed [00:19:57]:

So I've touched on this topic with, from our team, Jamie and Arun and Caroline, you know, and some extent Greg, who all have views on it. And I found that my views were a slight minority in our team. So I'm kind of saying this with an awareness of that, but I think that I just I do think we're going to see this split where there are going to be companies that are large enough as suppliers to dictate terms for sharing this data. And this is true today. Like there are these companies today and you know, we can like if you look at like a Cargill or an Archer, Daniel Mittlands, if you look, you know, relative to almost all their customers, they are twice as large, three times as large, in some cases 100 times as big in the packaging space. Similarly, right, you can look at all the big packaging companies, you know, whether it's the Amcors or otherwise, there are many of these situations where the supplier is actually significantly bigger than the customer, but the customer needs this data to complete their own emissions calculations and otherwise in those situations there are two reasons why it makes sense for there to be some monetization in my view. One reason is that right now a lot of the smaller customers struggle to get priority. They struggle to actually get the data because they, they are a small fry compared to, you know, let's say a Kraft Heinz, right.

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Saif Hameed [00:21:20]:

Or a Diageo or a larger customer. A big potato. Yeah, small fry compared to a big potato. And so, you know, they're in the queue with the big potatoes and they're never going to get the data that they need. Now maybe everyone, you know, switches over to a Pact based system and can kind of pull this, the Pact API, but you're still going to have this massive queue of just requests that someone needs to approve. So I think monetization gives a good way for those companies to actually say, hey, we're going to pay you something, not a lot, but something for this data. We should get this data quickly as a result. And whether that's a direct relationship between them and their supplier or there's a third party intermediary in between that kind of makes the market for it.

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Saif Hameed [00:22:01]:

I think that's an interesting space to explore. The second is that this data is not free for the supplier. Like the supplier needs to do stuff to get this data together. And the better you want this data to be, the more stuff the supplier has to do. Like it's not just about IT systems, it's about assumptions, analysis, supply chain engagement on their side. There's a lot of work that goes into this. And so we're not talking about significant sums in the context of their revenue, but it's not nothing. And so I think that some element of monetization allows the supplier to recover the costs of this data as well, which is another reason why it makes Some sense.

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Saif Hameed [00:22:39]:

Now, I don't think that we're going to get to a world where an emissions factor or a PCF costs you like $100 to request from a supplier, but I think a world where it costs you something like a non zero value that may be anywhere from $0.10 to $10, I think that's not a crazy scenario. So that would be something I would expect to see do tested in the next 18 months.

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Isobel Wild  [00:23:06]:

So do you think B2B brands are actually better placed to have more kind of opportunities off the back of sustainability versus B2C where the consumer perhaps doesn't necessarily care about the accuracy of their can of tomatoes?

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Saif Hameed [00:23:24]:

Yeah, for sure. There's an exception which is I think that if you're a challenger B2C player and you're building your brand on sustainability, sustainability, then sustainability can be a big margin creator for you. And we obviously see this with a number of companies that we talk about as well, the Oatleys of the world and others where sustainability is a core part of the brand and therefore you are getting value from it. But in general, I think there's a lot more space for B2B and the reason is actually not so much about this data business, but it's more about innovation. So if you look at packaging today, I think that packaging has a massive opportunity already to innovate and to bring new offerings to their customers on the basis of sustainability. Whether it's with an EPR regulatory mindset or a consumer mindset or otherwise. It's like a field day for new offerings, new pricing opportunities. There's a lot that you could be doing right now in that space right.

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Isobel Wild  [00:24:26]:

Onto our red herrings. And for people who don't know what a red herring is, a red herring is perhaps something that you assume to be correct but is actually misleading. So sev, our number one red herring is that there's a lot of talk about the potential of AI in improving climate data collection and reporting outcomes. Let's put this to bed. Saif, do you think in 2025 AI will make everyone's lives easier?

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Saif Hameed [00:24:56]:

So I think that this is a partial red herring, partial bet. And where I would come from is there's a lot of speculation that Gen AI in the enterprise community so in business is going to really transform how everyone does everything in practise. I think that Gen AI is going to be really interesting and really promising for a lot of the solution providers and we are certainly making sense, extensive use of it in our business already and we'll extensively continue to do so over this year. So it's going to be quite game changing for us, for the customer. If I think about the different use cases that right now cause frustration and pain, other than their engagement with solution providers, I don't see Genai making a huge difference. And the reason for that is the logical places where you could use Genai are in simplifying data collection and gathering, in let's say data enrichment and calculation and let's say in analysis of the data and generation of initiatives and targets and so on. So let's take these three categories in turn and talk about why Gen AI is a bit of a red herring this year and then we can reflect on next year. I think that from a data collection and data gathering perspective, what we're kind of waiting for is this agentic era within Gen AI which means that you have AI based agents that can actually not just apply reasoning and cognitive ability, but can also execute tasks.

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Saif Hameed [00:26:25]:

And you can basically say to your AI colleague, hey, can you actually reach out to the finance team for this data, reach out to so and so for that data, bring it together, QC it for me and if there's something missing go and reach back and get someone to fill in the details. That's like a proper agentic system where it's not just a one way street where you're asking a question, getting a response, but the AI agent is actually going out and implementing tasks and doing things differently. Now the problem is that right now the AI is not super reliable and means that the failure rate, you know, if the failure rate is like 10%, imagine it's going to do like five tasks in a sequence and every task is going to have a 10% failure rate that just compounds and I think business is too risk averse to actually try that at any kind of scale. And I think that means, and I think business is actually going to be a slower adopter of the technology than consumers. So let's say AI even reaches that level of capability this year. I think it's another year before it really makes it into a big corporate environment. The same problem holds back using Gen AI on the customer side, on the business, on the organisation side, for the enrichment and the calculation and so on. However, because of the way that let's say solution providers are set up, they might have humans in the loop, they might have better ways to industrialise it, they're a little more risk taking and so I think that might be a quicker adoption.

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Saif Hameed [00:27:52]:

On the solution provider side, if I look at the final piece, which is Initiatives, target setting and so on. I think one of the challenges right now is that you'd have to find a way to export all the finished data into some sort of a third party platform where you can kind of run this analysis. So you're sort of again, depending a little on the solution provider to have that ready for you. I think that'll be a little quicker to come into the fore. But the level of insight that you get there is only going to beat human insight or be comparable with human insight at quality and speed. If the volume of data that you have available for that analysis is too significant for a human being to kind of readily eyeball. We feel quite fortunate in that at Altruistiq we have these sort of vast data sets that we're ingesting for our customers, like 10 million records, 100 million records per customer, which is that level of scale where it's just significantly more than you could analyse at a human level. But I think there's going to be a bit of a lag there as well.

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Saif Hameed [00:28:52]:

So that'll probably be the first one that comes in. I think next year is going to be great for this space on the sustainability team side in business, but, but I think this year is going to move slower on business value from this directly than people might expect.

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Isobel Wild  [00:29:06]:

Onto our red herring number two, which is real time data collection is a core data management requirement. Saif, can you unpack that one for me?

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Saif Hameed [00:29:15]:

Yeah. So real, you know, data is not free and speed is not free as the world is rapidly recognising. If you look at, you know, we talk a lot about gen AI in this episode, but if you just look at the cost of like getting data available at speed, feed on tie all the time, it's expensive. And so I think that the value and upside of real time data is overblown for sustainability. And I say this as an environmentalist. I think that some companies expect that they will have their emissions data, let's say available all the time, up to date to the minute, which means that as things get updated in their systems, it is seamlessly updated in their environmental calculations. Let's say there are multiple reasons why this will be difficult and expensive, one of which is just simply data security. Again, usually large volumes of data come into platforms like ours through an API push from an SAP or an Oracle or another system as well.

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Saif Hameed [00:30:20]:

And that is more secure for the customer than for us to be acting data every minute, let's say, or every two minutes, which, you know, maybe we could do technically, but it's just like us basically blasting into the customer's data systems and just pulling what we need all the time, constantly. Which is just not how most IT teams would want us to work with companies. The other thing is that you obviously have this element of just how quickly can you process that data? Often these are very large volumes. In our case from individual customers, we might be receiving 10 million rows of data, 20, 30 million rows of data at a single push. Imagine just processing that afresh every minute. It gets a bit mind boggling. And then finally, anyone who's worked in this space knows that there's always a quality control element. You want to have some person eyeballing things, not because necessarily the emissions calculation is done wrong, but because there might be something funny in the source data, the source data might be missing something, the source data might be incorrect, etc.

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Saif Hameed [00:31:23]:

You don't want to have this data just updated all the time. So you just have this endless stream of quality control activities. If you balance that against the upside that you get from having this live stream of data versus having it, let's say, monthly or weekly or quarterly, I'm not convinced there is that huge upside. I would imagine that we're going to have some companies still trying to shoot for this, but most companies will settle for monthly or quarterly as the aspiration.

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Isobel Wild  [00:31:51]:

Thanks, Saif. So my final question, which we always tend to wrap up with, is what is your single most important piece of advice for professionals looking to get on top of these big bets as well as the red herrings going into 2025?

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Saif Hameed [00:32:06]:

I would say think about what you want next year to look like and kind of work backwards from that. I think that for me and for our business, we have been very sprightly and very agile to date, which means that our planning has been very ad hoc and very kind of phase based. Almost like, what's the next six weeks, what's the next couple of months? And we are also now trying to get into this frame of having a two year plan. And when you start taking this slightly longer term perspective, you then can say, well, what are the bets I need to make this year that are going to pay off next year? And what's the friction I'm going to deal with this quarter that I know I won't have to deal with in Q4? And I think that a lot of sustainability teams have been just so under the load of the stuff that they need to get done that they tend not to be thinking 18 months out or 24 months out. And I think that it is helpful to just take that lens now that we have a sense of a lot of the reporting requirements having somewhat stabilised. Like CSRD is settled, we kind of know what it is. The SEC stuff has settled. We sort of know what it is until it changes.

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Saif Hameed [00:33:14]:

And so, you know, you can almost start thinking, well what does two years look like and if I need something in place next year, how do I get it in place? What should I think about? What should I look for? I think you can start doing that planning over the next few months.

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Isobel Wild  [00:33:28]:

So it looks like data management is becoming more sophisticated and potentially bit more costly. We've hedged our bets on emission factors becoming commoditized as well as data security creating data silos. We've also mentioned our red herrings about AI and what the potential value of it is as well as real time data collection and how actually the value that you're going to get from that is probably incremental in terms of the effort you're putting in. Thank you so much everyone for listening. Goodbye Saif.

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