Impact Interview: Dan Graf, CEO of Earthchain

Can you introduce yourself and tell us about your role?

I’m Dan Graf, CEO of Earthchain.

I lead the product and the direction of the company. In practice, that means spending a lot of time with customers, understanding how sustainability, finance, and operations teams actually work, and shaping what we build so it fits into those workflows rather than sitting alongside them.

My background is in fintech. I spent around 20 years building products and working with financial data, where accuracy, traceability, and trust aren’t optional. That shaped how I think about systems. Over time, I became more interested in applying that experience to sustainability, where the data problems are arguably harder but just as important.

I went on to complete an MSc in Sustainable Development to properly ground that shift. That helped connect the theory with what I was seeing in practice, and ultimately led to starting Earthchain.

Earthchain provides the data foundation that sustainability teams rely on. Most organisations are still dealing with fragmented, unstructured data, spread across invoices, utility bills, logistics records, and internal systems. A lot of effort goes into manually stitching that together, and even then the outputs are often hard to trust or too slow to be useful.

We focus on that problem — turning messy, real-world inputs into continuous, structured, auditable data that can be used inside the business, not just at reporting time. The aim is to make sustainability data behave more like financial data: something that is reliable, traceable, and available when decisions are being made.

That changes how the work gets done. Instead of teams spending most of their time gathering and cleaning data, they can focus on understanding it and acting on it. It also makes the data usable beyond the sustainability team. Finance, operations, and leadership need to be able to rely on it if it’s going to influence real decisions.

My role is to make sure we stay focused on that layer — staying close to customers, translating what they need into product decisions, and making sure what we build holds up under scrutiny. That means being careful about data quality, auditability, and how the system behaves in real-world conditions, not just ideal ones.

We’re deliberately not positioning ourselves as another reporting tool or a broad “platform”. The value is upstream of that. If the underlying data is wrong or incomplete, everything built on top of it is compromised. If the data is solid, a lot of things become easier.

The other part of the role is making sure we’re building something durable. The sustainability market is still evolving, and a lot of the language around it is noisy. We stay anchored in the underlying problem, which hasn’t really changed: businesses need to understand what’s actually happening in their operations, and they need to be able to trust that understanding.

That’s what we’re focused on building.

How did your company come about, and what was the motivation behind it?

Earthchain came out of a fairly simple frustration.

When I started looking seriously at sustainability, it was clear that most of the effort was going into assembling data rather than using it. Teams were spending huge amounts of time pulling information from invoices, spreadsheets, and different systems, then trying to turn that into something coherent. Even then, the outputs were often based on estimates or assumptions, which made them hard to rely on.

Coming from a fintech background, that felt strange. In finance, you wouldn’t accept that level of uncertainty or manual effort as normal. The data layer is expected to be structured, traceable, and continuously available. In sustainability, that foundation just wasn’t there.

That gap was the starting point.

The motivation wasn’t to build another reporting tool. It was to fix the underlying data problem. If you can take the raw, messy inputs that businesses already have and turn them into something reliable and consistent, a lot of other things follow. Reporting becomes easier, but more importantly, the data becomes usable for decisions.

The early versions of Earthchain were very focused on proving that point — taking real documents, extracting the relevant information, and building a system that could produce detailed, auditable outputs without requiring teams to reformat everything first.

As we worked with customers, the pattern kept repeating. The constraint wasn’t lack of intent or awareness; it was the operational reality of dealing with fragmented data and manual processes. Once that was removed, the conversations changed. Teams could focus on what to do, rather than how to assemble the data.

That’s still the motivation now. Build the data layer properly, and make sustainability something that can operate inside the business, not just at the edges during reporting cycles.

Can you describe your company’s mission and values?

Our mission is to make sustainability something that operates inside the business, not just around reporting cycles.

In practical terms, that means building the data foundation that allows sustainability teams to work with the same level of confidence and continuity that finance teams expect from their data. If the data is fragmented, slow, or hard to trust, it limits what teams can actually do. If it’s reliable and available in real time, it becomes something that can support decisions.

That’s the problem we’re focused on.

In terms of values, the first is being grounded in reality. We build around how businesses actually operate, not how frameworks or tools assume they should. That shows up in working from primary data, dealing with messy inputs, and designing for real workflows rather than idealised ones.

The second is credibility. Sustainability data is increasingly being used in financial and operational decisions, so it has to stand up to scrutiny. We put a lot of emphasis on traceability, auditability, and making sure outputs can be trusted, not just generated.

The third is being deliberate about where we sit in the stack. We focus on the foundational layer — turning unstructured data into something continuous and usable. We don’t try to position ourselves as everything. If that layer is done well, it supports a lot of other use cases without needing to overstate what the product is.

There’s also a focus on avoiding noise. The market is full of broad claims around platforms and AI. We use those technologies where they make sense, but we’re careful to describe them in terms of what they actually do: extraction, validation, calculation. The value comes from the outcome, which is trusted, decision-grade data, not from the label.

Overall, the aim is to build something that is durable, aligned with how the market is actually evolving, and useful to the people who are accountable for delivering sustainability outcomes inside a business.

What are some of the most pressing social issues that your company is working to address through its technology?

One of the key issues we touch is trust.

A lot of sustainability data today is still based on estimates, proxies, or methodologies that are hard to interrogate. That creates a risk of greenwashing — sometimes intentional, often not — but the outcome is the same. Claims are made that aren’t properly grounded in evidence, and that undermines confidence across the board.

We focus on making the underlying data traceable and evidenced. If a number exists in the system, it can be tied back to a real-world source: an invoice, a bill, a logistics record. That changes the dynamic. Instead of relying on assumptions, businesses can point to where the data comes from and how it’s been processed.

That has a few knock-on effects. Internally, it gives teams more confidence in what they’re reporting and acting on. Externally, it makes it easier to stand behind claims, whether that’s to customers, regulators, or investors.

It also helps move ESG away from being narrative-led and toward being grounded in measurable, transparent performance. When KPIs are built on data that is visible and auditable, they become harder to manipulate and more useful for decision-making.

We’re not positioning ourselves as solving greenwashing directly, but by improving the quality and traceability of the data, we reduce the space in which it can happen.

How does your company measure the impact of its work in creating positive change?

We look at impact in terms of what changes for the customer once the data problem is solved.

The first signal is data quality and coverage. Are we moving a business from partial, estimated data to something that is complete, traceable, and based on primary sources? If the answer is yes, that’s a meaningful shift, because it underpins everything else.

The second is how that data is used. It’s one thing to produce a footprint; it’s another for that data to show up in decisions. We look for evidence that teams are using it to identify hotspots, challenge assumptions, prioritise actions, or engage suppliers. If the data is being referenced in operational or financial conversations, that’s when it starts to have real impact.

We also look at how much manual effort is removed. In many cases, teams are spending the majority of their time gathering and cleaning data. If that time drops materially, it frees them up to focus on reduction activities rather than administration. That’s a practical change, but an important one.

On the output side, we track the quality and defensibility of what’s being reported. Are the numbers backed by evidence? Can they stand up to scrutiny? That matters as expectations increase from regulators, customers, and investors.

Ultimately, the impact we’re aiming for is a shift from retrospective reporting to something more continuous and operational. When sustainability data is reliable and available in near real time, it can influence how a business runs, not just how it reports.

In your opinion, what impact will technology have in creating a better future?

Technology will matter, but only to the extent that it improves how decisions are made.

There’s a tendency to overstate what technology itself does. Most of the underlying problems aren’t a lack of tools — they’re a lack of reliable information and the ability to act on it. If technology doesn’t address that, it just adds another layer.

Where it does have a clear role is in handling complexity. Modern supply chains, energy systems, and production processes generate a lot of data, but much of it is fragmented or unstructured. Without technology, it’s not practical to turn that into something usable at scale.

If that layer is solved, it changes what’s possible. Decisions can be made with a clearer understanding of impact, trade-offs can be evaluated properly, and actions can be tracked against real outcomes rather than assumptions.

In sustainability specifically, that shift is important. There’s a move away from periodic, retrospective reporting toward something more continuous. Technology enables that by making data available in a form that can be used day to day, not just at the end of the year.

There’s also a governance aspect. As expectations increase — whether from regulators, investors, or customers — the ability to show where data comes from and how it’s been processed becomes more important. Technology can support that by creating traceability and consistency.

That said, technology doesn’t replace judgement. It supports it. The value comes from better information feeding into better decisions, not from the tools themselves.

If that balance is right, technology can play a meaningful role in improving outcomes. If not, it risks becoming noise.

What advice do you have for other companies looking to use tech for good and positively impact the world?

Start with a real problem, not a technology.

It’s easy to begin with what a tool can do and then look for somewhere to apply it. That usually leads to something that looks interesting but doesn’t get used. The better starting point is to understand where people are already struggling — especially where work is manual, slow, or unreliable — and build from there.

Spend time in the detail of how that work actually happens. Not how it’s described in a process diagram, but what people really do day to day. That’s where most of the constraints sit.

Be careful about adding complexity. A lot of systems fail because they expect clean inputs or new behaviours from users. If you can work with what already exists and reduce the effort required, adoption tends to follow more naturally.

Focus on credibility early. If what you produce is going to inform decisions, it needs to be trusted. That means being clear about where data comes from, how it’s been processed, and where the limits are. It’s better to be explicit about uncertainty than to hide it.

Avoid broad claims. The “for good” part isn’t something you declare; it’s something that shows up in outcomes. Be specific about what you’re changing and how you’ll know if it’s working.

Finally, stay close to the people using what you build. The feedback loop matters. Most of the useful insights come from seeing where things break or don’t quite fit, and adjusting from there.

There is a particular kind of impact company that doesn’t trade in slogans, and Earthchain belongs to that category. Dan’s framing — that sustainability’s central problem isn’t ambition or awareness, but the operational reality of working with broken data — quietly reframes a sector that has spent much of the last decade focused on narrative and disclosure rather than infrastructure.

The implication is significant. If sustainability data can be made to behave like financial data — continuous, traceable, decision-grade — then ESG stops being something a business reports on and starts being something a business runs on. That is a less visible kind of progress than headline pledges or net-zero targets, but it may turn out to be the one that makes everything else credible.

In a market crowded with platforms and proclamations, the discipline of building the layer beneath them is its own form of impact.