Sustainability data requirements are growing across Sweden, Europe and the world. The frameworks exist, the regulatory direction is setting, and the market is moving regardless of where any individual organisation sits in the compliance timeline. What each organisation does now determines what it will be capable of producing when the pressure intensifies.
A sustainability reporting process takes time to mature. Organisations that begin to build a structured implementation will have a first full reporting cycle complete, a second year of comparable data in hand, and a process their team can run confidently by the time mandatory disclosure requirements apply to them.
What capability actually consists of
Four components are required for a strong sustainability data and reporting process. When any one is underdeveloped, the others compensate in ways that can produce added challenges, and reduce reliability of data and business value.
Platform is the system that structures data with full traceability from raw input through calculation to reported output. A well-designed platform removes the dependency on one person holding all methodology knowledge and makes the historical record of decisions accessible to whoever needs it.
Process is the collection cycle that operates the same way each reporting period, with requests designed for the people answering them. A good reliable test of a well-built process is simple: can someone new run the next reporting cycle without calling the person who ran the last one?
People, meaning data owners across the organisation who understand what data they are collecting and why, a sustainability manager who can translate between sustainability logic and business language, and access to external expertise that brings the accumulated knowledge of where most processes stall and what specifically distinguishes the ones that advance.
Mandate is an explicit organisational decision that sustainability data collection is a requirement, communicated clearly from a level of authority that gives it genuine weight. Mandate is the component most frequently assumed to exist and most frequently absent in practice. Without it, collection depends on individual goodwill rather than organisational commitment, and the process remains fragile regardless of how well the other three components are configured.
One distinction worth making explicit before describing how capability develops: data coverage and data quality are not the same thing, but they are frequently treated as if they were. Coverage measures how much data an organisation has. Quality measures how reliable that data is and what decisions can reasonably be based on it. When the two are conflated, a process can appear comprehensive while resting on estimates throughout. Being clear about that difference internally, and being willing to show where the gaps are, is itself a sign of maturity.
Let us look at how capability can be built over time!
Year one: the decisions that define everything downstream
The first year is when the most consequential decisions get made, often without the people making them fully appreciating how long-lasting their effects will be. Three decisions above all others shape what the process will be capable of producing in year two and beyond.
Design data collection for the person answering it.
The default approach in most organisations is to build collection requests around what needs to be received, every data point the reporting framework requires, in the format most convenient to process further. This feels logical. However, it often produces low response rates, inconsistent formats, and data that requires significant manual cleaning before it approaches a usable state.
Effective data collection design can be supported by four specific decisions made before the first request goes out:
Scope each request to what that specific reporter can actually provide. A facilities manager and a finance director are not the same reporter and should not receive the same form or questions.
Time collection around the reporter's calendar, not (only) the sustainability team's deadline. Peak operational periods and quarterly closings suppress response rates in ways that create avoidable gaps in data coverage.
Collect static information once and reference it repeatedly. Meta data, organisational specifications and organisational structure do not change quarterly and should not be collected as if they do.
Make the purpose visible. Connect each data request clearly to why that specific input matters and what it feeds into. Reporters who understand the purpose of their contribution submit more carefully and engage more consistently over time.
Trying to measure everything at the same level of ambition typically means nothing ends up reliable enough to act on. The more productive question, and one worth answering explicitly in year one, is: which data needs to be high quality to drive the right decisions, and which is adequate at a rougher level? That is a strategic choice about where data quality investment goes, not a lowering of ambition.
Build continuity into the system from day one.
Relying on one person’s knowledge creates risk in any sustainability reporting process. If they leave, key methods and assumptions can be lost with staff turnover or absence. A system-based solution mitigates this risk by capturing submissions, methodologies, and decision rationales in a centralized, accessible record, ensuring seamless handover and preservation of knowledge. This requires consistent and real time documentation of key decisions such as emission factor selection, methodological changes, and the use of estimates resulting in a transparent, traceable, and audit-ready dataset.
A practical step that supports both continuity and ownership is building a shared logical data model with all relevant functions, agreeing on what each data point is called, what it measures, and who owns it. This aligns the people doing the reporting with the purpose behind it, and creates a common reference that does not depend on any one person's memory.
Establish genuine mandate.
Collection design can be excellent, the platform well configured, and the process carefully built, and the process can still fail to produce reliable data. The reason is almost always the same: the organisation has decided, implicitly, that participation is voluntary rather than required.
Genuine mandate requires a decision made explicitly at a level of authority that is credible to the people being asked, and communicated clearly downward. When that decision exists, the sustainability manager's role changes substantially. The energy previously directed toward individual persuasion can instead go toward process design, data quality improvement, and building the analytical capability that makes the data useful. That reallocation of effort is one of the most significant efficiency gains that comes from establishing a mandate properly.
One practical way to build and sustain mandate is to connect sustainability data to economic outcomes. Twelve thousand tonnes of CO₂ means little to most decision-makers. What that figure costs, what an intervention would save, what the consequence looks like in financial terms: that creates a different kind of weight. Data that speaks the language of the people who hold the decisions travels further than data expressed only in sustainability units.
Year two: when the data starts to earn its investment
The second year builds from the first, and now the data starts to support questions it was previously too unreliable to answer with any confidence.
Management teams with consistent, comparable year-on-year data begin asking things that could not be answered before:
Why is energy intensity at one facility significantly higher than at comparable sites?
Based on the year-on-year trends, which business units are on track toward the 2030 target, and which require active intervention?
Where in the Scope 3 footprint does the greatest reduction potential sit relative to the cost of acting on it?
These are the questions that distinguish a reporting process operating as a management intelligence system from one operating as a compliance function. They are also the questions that reveal whether the decisions about data strategy, collection granularity and roles made in year one were adequate.
One structural challenge that becomes more visible once the process is running: sustainability data typically takes a quarter to arrive, while financial data is available the next day. That difference in lead time means sustainability inputs consistently fall outside the window in which operational decisions are actually made. A well-built data foundation shortens that lag over time, and in doing so moves sustainability data from a retrospective reporting function toward something that can actually inform decisions as they happen.
Year two is also when the economic logic of a well-built foundation becomes visible in practice. The same underlying dataset can begin serving multiple outputs simultaneously: the sustainability report, the management dashboard, green loan requirements, and customer ESG questionnaires. One collection effort supports all of them because the data structure was designed for reuse rather than single-purpose extraction, saving time, resources and in the end reducing cost. The investment in year one begins distributing its return across the outputs it enables.
Year three: from reporting process to governance tool
The destination of a mature sustainability process is replicability, and a state in which data informs decisions before they are made rather than documenting them after the fact. The common thread is that reliable data changes what decisions are possible to make. Many organisations have taken significant sustainability commitments on the basis of data that was considerably more volatile than they understood at the time. When the underlying data is reliable, the decisions built on it are robust.
Six signals that your sustainability data reporting process is developing and your capability is increasing
Progress in sustainability data capability is observable and measurable. These six signals, tracked over time, provide a reliable indication of whether a process is genuinely developing
Collection is replicable. New team members can follow the process from system documentation and established workflows rather than from the institutional memory of whoever ran the previous cycle.
Ownership is specific and named. Each material data flow has an identified owner in finance, operations, facilities, or human resources who is accountable for its accuracy.
Questions from reporters are declining year on year. Reporters are developing confidence in both the process and its purpose. A mature process generates almost no inbound clarification requests.
Primary data is increasing as a share of total. The mix is shifting from spend-based estimates and generic emission factors toward activity-based, source-specific inputs as supplier relationships mature and internal collection processes improve.
The same data serves multiple outputs. Sustainability report, management dashboard, loan covenant, customer questionnaire, all from one collection effort.
The data is influencing consequential decisions. Procurement choices, capital allocation priorities, supplier relationship structures, and operational policies that continue to generate value beyond the reporting cycle in which they were made. This signal arrives last and matters the most.
Most organisations are further along than they realise, and closer to a reliable process than they think. We can help close the gap, make it more efficient and ensure you spend time on what makes an actual impact!
