A law firm, an accountancy practice or a consultancy has no production line, no vehicle fleet of any significance, and usually no premises of its own beyond an office. The emissions do not sit in machinery but in people: office buildings, laptops, business travel, and the services bought in from others. Where an industrial company can measure most of its emissions within its own walls, at professional services firms the largest part sits precisely outside those walls. That shifts the question from "what do we emit" to "what do the parties around us emit", and that shift is exactly where the data becomes difficult.
The basic data on the firm's own premises can often still be found: energy invoices with facilities management, service contracts with the building manager, lease agreements with finance. But as soon as the scope widens, the data falls apart across departments that do not necessarily talk to each other. Travel costs and expense claims sit in the HR or expenses system, often without any distinction between air travel, train or car. Purchasing of IT services, cloud storage and software licences runs through procurement or through separate departmental contracts, with invoices that do not state the supplier's energy consumption. Paper use, printing and catering sit with facilities. Temporary staff and freelancers are sometimes included and sometimes not in the personnel figures, depending on how the HR administration is set up.
The largest item, services bought in from other companies, is usually the vaguest. A consultancy that engages subcontractors, an accountancy practice that outsources work to another firm, a legal team that hires in outside experts: that chain of service delivery generates emissions that are not automatically recorded anywhere, because the invoice only shows the amount and not the origin.
Because there is no device that measures continuously, the sustainability data at professional services firms often comes about through manual collection: someone requests invoices from facilities, requests travel data from HR, requests purchasing data from procurement, and pastes it all together in a spreadsheet. That spreadsheet becomes the de facto system of record, without anyone being able to point to who manages the source of each figure or how often it is updated. In the next reporting round the work is often done all over again, with a slightly different selection of sources, which makes the figures difficult to compare from year to year.
The temptation is to purchase a reporting tool as soon as the questionnaires start coming in. But a tool laid on top of a scattered, manual collection process mainly makes the result look tidier, not more reliable. The underlying question then remains unanswered: who owns the travel data point, where does the building's energy figure come from, which quality rule determines whether an excluded freelancer counts or not. Without a register that records, per data point, where it originates, who manages it and which rule safeguards its quality, every report remains a snapshot of what someone managed to collect that month.
The Data Readiness Scan from csrdready.net maps that register for this specific sector: for each data point the source systems, the path from source to report, the owner within the organisation, and the rule that determines whether the value is correct. For an office-based organisation that often means a combination of facilities systems, HR administration, procurement and manual input, each with its own rhythm and its own manager. The scan records that combination so that it does not have to be reinvented every year.
The way this fragmentation arises differs by sector. Those who want to compare what this looks like for similar issues can look at the spread of energy and purchasing data in retail, at the role of customer journeys and server data in the ICT sector, or at the interweaving of investment data and own operations at financial services providers. The patterns differ, but the core is always the same: data that arises in systems that were not built with reporting in mind.
The Data Readiness Scan is currently being built. There is no working product to go through today, and nothing is being sold here that does not yet exist. Those who want to establish for their own organisation where the data point register should begin can sign up for the waiting list and will be kept informed once the scan becomes available.
Once it is clear which data point comes from which system and who is responsible for it, attention often shifts to the question of how much of the collection work itself still has to be done manually. Requesting invoices, merging spreadsheets and checking figures are tasks that can be broken down into the part that follows a fixed pattern and the part that requires judgement. FTE TO AI calculates, per task, what share of that work can be taken over by AI, as a follow-up step once the data point register is in place.
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.