An educational institution is not a factory and not a retail chain, but it is an organization with buildings, energy contracts, transport, procurement and a large group of people who are not employees in the conventional sense. Students and pupils move through buildings, use facilities and generate travel movements, without appearing on any payroll. This ratio between employees and users of the buildings is different in education than in most sectors, and this carries through into the data: a large part of the environmental impact is tied to occupancy and building use, not to a production process or a shop floor.
On top of that, an institution often consists of multiple locations, sometimes spread across a city or region, each with its own meters, its own management and its own lease contracts. Education also has a strong seasonal rhythm: holiday periods, interim vacancy, evening use by third parties. Anyone who adds up energy data per month without knowing that rhythm can easily draw the wrong conclusions about savings that are actually just holiday spread.
At most institutions, the building stock is the largest item. That data is spread across several places: the facilities department has energy contracts and meter readings, often per building and not centrally summarized. At larger institutions with their own real estate, a building management system runs through this with consumption data per installation. At rented locations, part of the responsibility, and with it part of the data, lies with the landlord, who is not always willing or able to share it at the level required.
Renovations, insulation measures and the greening of installations are often recorded in project files held by the facilities department or an external party, separate from the regular energy administration. Anyone wanting to see the effect of such a measure reflected in the figures has to link these two sources together, and that rarely happens automatically.
Staff commuting, if measured at all, is usually collected via HR or an expense system. Travel behavior of students and pupils is a different matter: that data often does not exist in a central system, at best as an estimate based on postal code areas or a survey conducted a few years ago. In higher education, international travel is added to this, for exchange or research, recorded in separate administrations by international offices or research departments, separate from the regular travel data of staff.
Anyone who does not keep these two groups — employees and students — separate in the data point register risks adding apples and oranges: a single full-time employee weighs differently in the data than a student who comes to one building two days a week.
Procurement at educational institutions often runs through a central procurement department for large contracts, but alongside decentralized orders by faculties, departments or individual research groups. Laboratory materials, chemicals and specialized equipment at research institutions have their own procurement chain with their own suppliers, separate from regular office procurement. Catering is usually outsourced to an external party, which means data on food waste or the origin of ingredients does not lie with the institution itself but with that supplier, with varying willingness to share figures.
Waste follows a similar pattern: separated collection is organized by the facilities department or an external waste processor, and the reports that result from this do not always match the classification needed for sustainability reporting.
At universities of applied sciences and universities, research adds a layer that other sectors do not have. Research groups often work with their own budgets, their own procurement and sometimes their own buildings or laboratories, financed by external parties with their own reporting requirements. This decentralized autonomy is functional for research, but it means central data owners often do not know what information is already recorded within those groups, or under what name and in what format.
At many institutions, the approach to sustainability data starts with the purchase of a reporting system. That system delivers tidy screens, but the underlying question remains unanswered: does the meter reading for building B represent office space, a sports hall or a part rented out to a third party, and who within the organization can answer that question. Without an organized picture of where each data point originates, who is responsible for it and which quality rules apply to it, a tool mainly produces neater displays of the same uncertain figures.
The Data Readiness Scan is therefore not a reporting tool but a register: per data point the origin, the path from source to report, the owner within the institution and the rules the data must meet. That register makes visible, for example, where student travel is missing, where the facilities department and building management count independently of each other, and where an external caterer or waste processor does not yet have a point of contact for data delivery.
The same question arises with its own characteristics in retail, the agricultural sector and financial services, each time with a different division between central and decentralized.
This scan is still under development. Institutions that want to know exactly where their sustainability data originates and with whom it resides can sign up for the waiting list and will be informed as soon as the scan becomes available.
Once it is clear which data points exist, who manages them and along which path they come into being, it also becomes visible what work lies behind them: retyping meter readings, tracing travel data from spreadsheets, checking caterers' invoices for the origin of ingredients. Much of that work is repetitive and tied to fixed sources, which makes it suitable for assessment for 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 same type of task breakdown used in the data point register.
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.