Hospitality differs from many other sectors in that operations are spread across locations that each have their own meters, contracts and suppliers. A chain with multiple locations does not have one energy connection but dozens, not one waste contract but a patchwork of local agreements. Where a factory measures most of its emissions in one place, hospitality is divided across kitchens, refrigeration installations, terrace heating and laundries that are each registered separately, often with a different party than the head office.
Added to this is the fact that a large part of the relevant data does not originate at the business itself, but at suppliers: the wholesaler that delivers food, the laundry that processes linen, the brewery that supplies kegs. The ratio between what a hospitality business measures itself and what it must retrieve through the chain therefore turns out structurally different than in a sector with its own production. The largest part of the climate impact is not located in the building, but in what goes in and out of it.
Energy consumption is usually the first data point one wants to report, and immediately the point where the spread begins. A restaurant with its own kitchen has a different consumption profile than a hotel with a laundry and swimming pool, and a chain with rented premises often has no direct access to the meter readings because the landlord manages the contract. Invoices are addressed to the location, to the holding company, or to a third party that manages the meter. Whoever wants to add up the total consumption of a chain must first find out which meter belongs to which location and who receives the bill for it.
For many hospitality businesses, food procurement is the largest source of chain emissions, and at the same time the least structured data source. Orders run through wholesalers, local suppliers and sometimes directly with producers, each with their own way of invoicing and their own degree of detail about origin and processing. A delivery note tells what was delivered, not necessarily where it came from or how it was produced. Whoever wants to extract a data point here discovers that the source of the emission factor is often missing and that different locations purchase the same raw material from different suppliers, with different underlying assumptions.
Waste collection in hospitality is usually organised locally: each location has its own contract with a collector, sometimes even multiple for organic waste, glass and residual waste. Food waste is at best tracked per location in a point-of-sale system or a separate registration form, and at worst not tracked at all. When a reporting obligation asks for a figure on waste or waste separation, that figure must first be compiled from all those separate, local registrations, with the risk that the definition of "waste" differs from location to location.
HR systems keep records of employees, schedules and turnover, while facility or property systems manage the data about the building: square metres, heating installations, insulation. Both are relevant for sustainability reporting, but are rarely linked. A chain that wants to know how many square metres per employee are heated discovers that these two pieces of data sit in two systems that are not set up for this purpose and therefore do not automatically come together.
The sum of these sources is a landscape of meters, invoices, delivery notes and local registrations that are each located somewhere else, with ownership that is not always assigned. No one is, by definition, working on organising these sources; they simply exist, spread across locations and systems. Before a figure on energy, food or waste can be considered reliable, it must first be clear which system is the source, who is responsible for it and which rule determines whether a value is plausible. This applies to hospitality just as much as to other sectors; whoever wants to make the comparison can also read where the sustainability data in the agricultural sector comes from, where the chain of suppliers plays a similar role, or how the real estate sector deals with dispersed building data, given the overlap in housing data. Whoever wants to know exactly which data points are actually needed for their own operations rather than what a standard list prescribes can find more on the page about which data points you actually need with multiple locations or business units.
The temptation is to purchase a reporting tool as soon as the request for figures comes in. A tool placed on top of a disorganised collection of meters, invoices and local registrations produces a neater report about the same uncertain figures. The Data Readiness Scan is intended for the part that comes before that: a register of the data points a hospitality business needs, with, for each data point, where it comes from, who manages it and which rule determines whether the value is correct. That scan is under development; anyone interested can sign up for the waiting list.
Once it is clear which data points exist and where they need to come from, the question of who will from now on carry out the collecting, checking and entering of that data follows naturally. That work consists of a series of recognisable tasks, from reading invoices to merging location data, and not every task requires the same amount of human judgement. The work scan from FTE TO AI calculates, per task, what portion of it can be taken over by AI, so that it becomes clear where people remain needed and where the work can be automated.
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.