In a group with multiple entities, sustainability data is usually collected per location, per country, or per business unit. Someone at the location fills in a spreadsheet, sends it to headquarters, and there everything is merged into a report. That process works, until something goes wrong. Then it turns out that nobody knows exactly who was responsible for the step in between: the conversion of raw meter readings into an emission factor, the choice of which fiscal year a figure belongs to, the correction of a double count between two entities.
The question "who owns this process" is different from the question "who supplies this figure". Supply is visible: someone sends a file. Ownership of the process is invisible, until it is missing. Who decides which definition is valid when two entities follow a different approach? Who flags that a value falls outside the expected range before it reaches the report? Who can be held accountable when an auditor asks how a number was built up?
With a single entity, there is usually one person who oversees everything, even if that oversight is informal. With multiple entities, that informal coverage disappears. Every location has its own way of working, its own systems, its own people who have always done it that way. Headquarters sees the end result, not the process behind it. Without an explicit assignment of ownership, a group emerges in which everyone assumes someone else is guarding the bridge between raw data and the reported figure.
This plays out on multiple levels at once. There is the question of who owns the definition of a data point: which entity determines exactly what "scope 2 consumption" means when the energy supplier differs by country. There is the question of who owns the control: who checks that a value has been approved before it moves on. And there is the underlying question this article addresses: who owns the process itself, from source system to report line.
Without an owner, the process becomes a collection of habits. Someone adjusts a formula in a spreadsheet because it seemed more convenient, and nobody checks whether that adjustment was also implemented elsewhere. A location switches accounting systems and the new export has a different column order, which nobody notices until the figures no longer add up. A new employee takes over the reporting and follows the instructions of a predecessor who has since left, without knowing why a particular step was set up that way.
The consequences usually only become visible at the moment of external scrutiny: during an audit, when a regulator asks a question, the first time a figure has to be substantiated. Then it turns out that nobody can reconstruct how a number came about, because nobody had that responsibility formally. This is exactly the subject of what you do when nobody owns it in a group with multiple entities: not a question of blame, but a structural void that arose from growth, mergers, or simply time.
It is tempting to solve this problem with an organizational chart: one name next to one department. That does not work, because the process behind sustainability data does not consist of a single step. There is the source registration, the conversion into a unit or factor, the check on valid values, the consolidation across entities, and the final recording in the report. Each step can have a different owner, as long as that is explicitly recorded and not assumed.
What helps here is not a vague division of tasks but a concrete register: per data point, the question of who manages the source, who performs the conversion, who approves the outcome. This also includes the question of what constitutes a valid value, because without a shared definition of what a valid value is, no owner can check whether a figure is correct before it moves on. And without a way to flag deviating figures, as described under setting up a deviation signal, control remains dependent on whoever happens to review the figure again.
Once ownership of the process has been assigned, per data point and per step, a different kind of insight emerges: precisely who performs which action, how often, and based on which source. That overview makes possible a follow-up question that goes beyond who is responsible, namely which part of that work can subsequently be taken over by automation. The work scan from FTE TO AI calculates per task which part of the work can be taken over by AI, based on the tasks that became visible after ownership was assigned. Without that assignment, there is no task to scan, only a process that nobody can explain.
The Data Readiness Scan, which maps the data point register, the lineage per data point, and the ownership, is currently being built. Anyone who wants to join the waiting list for access as soon as the scan becomes available can sign up. There is no tool to order right now, but there is a place to be the first to hear when it is ready.
Vraag maar waar een datapunt vandaan komt. Dat is meestal de hele vraag.
Answers come from this site’s knowledge base. Not tailored advice, and not a scan of your company.