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Across food, agriculture and industrial supply chains, the conversation around sustainability has shifted from ambition to execution. Most organisations are no longer asking whether they should measure emissions or understand their supply chains. The question is how to do it in a way that is consistent, reliable and usable in day-to-day decision making.
This is where many programmes slow down. Data exists, often in large volumes, but it sits across systems, formats and teams. It is incomplete, inconsistent and difficult to reconcile. The challenge is not access, but structure.
Other sectors have already worked through similar problems. In healthcare, for example, large and complex datasets have long been part of routine operations. Bringing those datasets together, validating them and using them to support decisions is not straightforward, but it is well understood.
This article looks at what distinguishes a mature data environment, why sustainability is now moving in that direction, and what that shift makes possible for supply chains.
Why is sustainability data still difficult to use?
When organisations describe the difficulty of working with sustainability data, the language is often specific to carbon, Scope 3 or supply chains. In practice, the underlying challenge is more general.
Data is distributed across multiple systems. It is collected for different purposes, at different levels of detail and with varying degrees of quality. Bringing it together requires interpretation as much as extraction.
In healthcare, similar conditions apply. Clinical data is generated through imaging systems, patient records, laboratory results and monitoring devices. Each source has its own structure, its own constraints and its own potential for error. Yet the requirement to aggregate that data and draw meaningful conclusions from it is non-negotiable.
The comparison is instructive. The problem is not unique to sustainability. It is characteristic of any domain where complex, real-world systems need to be understood through data.
What does a mature data environment actually look like?
In more established data environments, maturity is less about cleanliness and more about consistency.
Healthcare systems provide a useful reference point. Data is rarely perfect. Records can be incomplete, measurements can vary and systems are often built over time rather than designed from first principles. What distinguishes them is the presence of shared structures and expectations.
Information is stored in defined systems. Processes exist for validating and aggregating it. There is a common understanding of what constitutes a reliable output and how it should be interpreted.
The consequence is that, even in the presence of complexity, decisions can be made with a degree of confidence. The effort is not spent attempting to eliminate every inconsistency, but in ensuring that the overall system produces results that are robust enough to act on.
Why has sustainability data taken longer to mature?
Sustainability data has developed under different conditions. For many years, measurement was fragmented. Companies reported selectively, methodologies varied and the boundaries of what should be included were not always clear. Data collection was often driven by individual initiatives rather than a shared framework.
As a result, systems evolved unevenly. Some organisations invested heavily in data capture and analysis, while others relied on estimates or partial information. Comparability was limited and confidence in outputs varied.
This has begun to change. Frameworks such as the Greenhouse Gas Protocol and initiatives linked to science-based targets have introduced a more consistent language. Scope 1, Scope 2 and Scope 3 emissions are now widely understood, even if implementation continues to evolve.
At the same time, the range of available data has expanded. Satellite monitoring, supplier reporting and production-level data are becoming more accessible, increasing both the volume and the relevance of information.
Do you need perfect data to make decisions?
One of the more persistent misconceptions in sustainability is that data needs to be complete and fully aligned before it can be used effectively. Experience from other domains suggests otherwise.
Enterprise systems, whether in healthcare or manufacturing, are rarely orderly. They are built to support operations, not to provide clean analytical datasets. Fields are used inconsistently, definitions shift over time and integrations introduce further complexity.
The challenge, therefore, is not to impose order at the point of collection, but to build processes that can interpret and structure what already exists.
In practice, this involves establishing clear rules for aggregation, understanding the limitations of each dataset and applying consistent logic when combining them. Over time, these processes create a layer of reliability above systems that remain, at their core, imperfect.
The role of domain understanding and expertise
Technical capability alone is rarely sufficient in environments of this kind. Data does not exist in isolation; it reflects physical processes, operational decisions and industry-specific constraints.
In healthcare, this is evident in the relationship between data scientists and clinicians. Interpreting an image or a signal requires an understanding of anatomy, physiology and diagnostic practice. Without that context, patterns in the data are difficult to interpret correctly.
A similar dynamic is emerging in sustainability. Emissions data is tied to production methods, logistics, raw materials, processing techniques and agricultural practices. Understanding how milk is produced, how ingredients are transformed or how supply chains are structured becomes as important as the data itself.
This is where progress tends to accelerate. Once technical and domain knowledge are combined, it becomes possible to question assumptions, refine methodologies and improve the quality of outputs in a meaningful way.
Frameworks are leading to structured data
Sustainability data is moving into a phase where structure is beginning to take hold.
There is greater alignment on what should be measured and how it should be reported. Companies are investing in systems that capture data more consistently, even if those systems remain complex. External expectations, whether regulatory or commercial, are reinforcing the need for credible and comparable information.
At the same time, the tools available for working with data have advanced significantly. Techniques that were once confined to specialised domains are now more widely accessible, allowing organisations to process and analyse large datasets more effectively.
Taken together, these developments are reducing the distance between data collection and decision making. Information that was previously used for reporting alone is starting to inform how supply chains are managed.
Good sustainability data leads to effective decisions
As sustainability data becomes more structured, its role begins to change.
Rather than sitting alongside operations as a reporting requirement, it can be integrated into procurement, production planning and commercial decisions. Differences in emissions, sourcing or processing can be identified with greater clarity and, over time, reflected in how supply chains are configured.
This does not require perfect data. It requires sufficient consistency to compare options and sufficient confidence to act on those comparisons. In that sense, the transition is already underway. The challenge is less about building entirely new systems and more about making better use of those that already exist.
Sustainability data as a catalyst, enabler and commercial tool
The trajectory is familiar. Other sectors have moved from fragmented, inconsistent data towards systems that support routine decision making. Sustainability is following a similar path, shaped by its own constraints but increasingly aligned in structure.
What changes as a result is not only how data is managed, but what it enables. Once information can be trusted and compared, it becomes possible to allocate it, trade it and use it to optimise outcomes across a supply chain.
For organisations working with complex, multi-layered systems, that shift opens up a different set of possibilities. Sustainability moves closer to the core of operations, where it can influence choices rather than simply describe them.
Turning sustainability data into commercial decisions
As sustainability data becomes more structured, the question shifts from measurement to use. For many ingredient suppliers and industrial producers, the challenge is no longer how to collect data, but how to apply it in a way that supports sourcing, pricing and customer conversations.
Segmos works with supply chains to make sustainability data usable. That means structuring complex datasets, linking them to real supply flows, and enabling them to support practical decisions across procurement and commercial teams.
If you are looking to move beyond reporting and understand how sustainability can be embedded into your commercial model, explore how Segmos supports the transition from data to decision.
