Monetizing Data Granularity in Nature-Related Risk Assessment

What is granularity?

Before diving into monetization, it’s important to understand what data granularity actually means.

Granularity refers to the level of detail in a dataset. High granularity means the data is highly specific, for example, satellite imagery at 3-meter resolution, daily soil moisture readings, or carbon measurements taken on individual farm plots using carbon probes or composite soil analysis. Low granularity, on the other hand, describes more aggregated information, such as national-level forest cover or regional water risk indices.

This distinction is critical. The level of granularity shapes how data is interpreted, what kind of decisions it can support, and how much value it holds for different users. In the context of nature-related risks and opportunities, it directly influences both the relevance of the data and its return on investment.

Data has a Return of Investment (ROI)

Data acquisition can be very expensive in some cases. So yes, data has its own ROI, one that depends on the cost of acquiring it, the value it can unlock, and how it is monetized. That value comes from how the data is used: whether it serves a broad risk screening or supports more advanced applications like certification or performance-linked financing.

In short, there is a clear ROI logic behind data: the cost to generate it versus the value it delivers. And that value changes depending on the level of granularity and the stage of the risk management process you are working on.

One of the most relevant frameworks guiding companies in this space is the Taskforce on Nature-related Financial Disclosures (TNFD). TNFD helps organizations identify, manage, and disclose nature-related risks and opportunities by providing a structured process known as the LEAP approach: Locate, Evaluate, Assess, and Prepare.

Low granularity data within a TNFD framework

Let’s take the Locate phase of the TNFD’s LEAP approach as an example. In this first stage, companies often rely on publicly available databases to map potential nature-related risks across geographies. These sources usually offer low to medium granularity and are particularly useful for early-stage assessments or broad materiality screenings.

Some commonly used public databases include:

• IBAT
• ENCORE
• Global Forest Watch (for deforestation alerts)
• Aqueduct by WRI (for water risk)
• GBIF (for species and biodiversity presence)
• FAOSTAT or national-level agricultural data

These datasets help companies understand their general exposure to risk at a country, regional, or sometimes sub-national scale. For example, you might learn that operations fall within a water-stressed watershed, but not whether specific supplier farms are driving or suffering from the risk.

In this context, data is mainly used for compliance. It supports disclosures under frameworks like TNFD by identifying hotspots and flagging potential liabilities. The monetization here is indirect. It comes from reduced compliance costs, faster internal reporting, and lower risk of reputational or regulatory damage.

When a company contracts a service to support this process, the ROI of the data should be evaluated based on two key factors: the amount of time saved in producing reports and the level of risk identified in relation to the company’s actual exposure and compliance needs. If the data allows the company to meet reporting requirements efficiently and avoid unnecessary liabilities, the investment can be justified. even if the granularity is limited.

However, the value is still constrained by how detailed the data is.

These databases won’t help you make precise decisions or take targeted actions especially for site specific business operations like agriculture.

Moving toward higher granularity data in assessing risks and opportunities within and beyond TNFD

That’s where the next phases of the LEAP framework come in, Evaluate, Assess, and Prepare. These stages usually require much more detailed, higher-resolution, and localized data. Once broad risks are identified, companies need to understand what’s really happening on the ground. What are the actual dependencies on ecosystem services? What’s the magnitude of risk? What should be done next?

This shift often involves using field-collected data, high-resolution satellite imagery, farm-level practices, and even real-time IoT sensors. The cost of data increases, but so does its value. The return is no longer just about compliance, it’s about making better decisions, avoiding operational disruptions, reducing insurance costs, and even unlocking financing.

At this point, data becomes a real risk-reduction asset. Investing in better granularity can help companies avoid millions in potential losses or delays. It can also speed up internal planning and reduce the uncertainty that often holds back investment decisions.

If the data is structured and centralized effectively, it can also be monetized in other ways. This is a critical point, as many companies still gather datasets across different departments for one-off, isolated purposes. That fragmentation limits value. For example, certification systems often require detailed, verifiable data to confirm environmental outcomes. When this is done right, higher granularity data delivers on risks definition and quantification and can unlock new revenue streams through sustainability-linked financing, value-aggregating certifications, or compliance-grade environmental credits.

On the other hand, if a company lacks a well-organized data infrastructure, operationalizing any of these opportunities becomes more complex, time-consuming, and expensive, often leading to missed benefits or duplicated efforts. Centralization is not just about efficiency; it’s about unlocking the full strategic and financial value of your data.

Going even deeper, ultra-high granularity data, the kind required for carbon or biodiversity credits, can open the door to new revenue streams. These projects involve higher upfront costs due to strict monitoring methodologies, but they come with clearly defined price points and income potential.

To summarize:

Each level of data brings a different cost-benefit profile. The key is to know where you are in your process and match the granularity to your goal, whether it’s compliance, risk mitigation, or direct revenue generation.

Moving into competitive advantage through Business Intelligence

However, there is one more layer where data granularity con add significant competitive advantage: Business Intelligence.

Once this dataset is created, if it is well-structured, homogeneous and centralized, it becomes the foundation for building solid Business Intelligence. This doesn’t just speed up compliance or reduce exposure to regulatory risks. It gives companies a powerful tool to improve operational performance, identify inefficiencies across their supply chains, anticipate climate or nature-related disruptions, and ultimately gain a competitive advantage. With the right structure, the same dataset can be used across departments, from ESG and procurement to finance and investor relations, turning sustainability into a strategic asset rather than a reporting burden.

If you need help in structuring multi-layered, reusable datasets we can help: LandPrint can help you structure, centralize, and activate your environmental data across all levels of granularity. Whether you’re mapping risk exposure, preparing for certification, or building a business case for nature-based investments, our platform turns complex data into actionable intelligence, helping you reduce risk, unlock new revenue, and gain a strategic edge. Get in touch with us if you are interested to learn more.