Retail has a characteristic that makes its sustainability data more difficult than that of a company with a single production site: most of the impact does not arise in its own buildings, but with others. A retailer sells products that were made elsewhere, transported by parties that are not employed by it, and packaged in materials chosen by suppliers. Its own stores and distribution centres only supply part of the total picture. The rest sits in a chain of suppliers, logistics providers and sometimes their own sub-suppliers in turn. Anyone looking for the sustainability data of a retail organisation is therefore largely looking for data that was not measured by the organisation itself.
The most accessible part of the data belongs to the organisation's own property footprint: energy consumption of stores, distribution centres and offices, often found in invoices from energy suppliers or in building management systems. For a chain with many branches, this quickly becomes a collection of separate contracts, meter readings and invoices per location, managed by different regional managers or a facilities team. The data exists, but is scattered across so many locations that nobody automatically turns it into a whole.
Transport data — fuel consumption of the organisation's own fleet, but above all of hired carriers — largely lies outside the organisation. A retailer that outsources transport receives invoices and sometimes reports from the carrier, but rarely in a format that matches its own reporting needs. This data has to be requested, translated and linked to internal volumes, which adds a manual step that easily gets lost between procurement, logistics and sustainability.
The largest part of a retailer's climate impact usually lies in the production of the goods sold, far upstream of its own door. This data, if it is available at all, comes from suppliers who use varying measurement methods themselves, or via generic industry averages that procurement or sustainability once looked up. With an assortment of many different products and suppliers, a spreadsheet emerges containing data of varying quality: some figures are measured, others estimated, others copied from an old report. Nobody within the organisation has a complete overview of where each figure originates.
Packaging data — weight, material, recyclability — is often recorded by procurement or packaging specialists, per product category or supplier contract. Waste data from stores and distribution centres in turn comes from waste processors, with their own reporting formats and units of measurement. Both streams rarely end up in the same system as the energy or transport data, meaning that anyone wanting a complete picture has to go through several departments to gather the pieces of the puzzle.
Alongside climate data, sustainability reporting also calls for social indicators: staff turnover, diversity, working conditions in the chain. For the organisation itself, this usually sits with HR, in systems that were not set up with sustainability reporting in mind. For the chain — working conditions at suppliers — the data is often even scarcer and dependent on audits or certifications that are not carried out in the same way every year.
The common thread in retail is that data rarely originates in one place and is almost never managed in one place. For every data point, the relevant question is: does this come from an in-house system or from a third party, who supplied it, how was it measured, and when was it last updated. Without a register that answers these questions, the data underlying a report remains a collection of separate files whose origin nobody can point to with certainty. That register is precisely what the Data Readiness Scan is aimed at: not the report itself, but the lineage per data point, the ownership and the quality rules that determine whether a figure holds up.
The challenge described here recurs in similar form in other sectors, albeit with different points of emphasis. For a hospitality business, the focus is on food flows and energy consumption per location; for an ICT company, on data centres and equipment use; and for a real estate business, on the energy performance of buildings throughout their entire lifespan. In each of these sectors, the same basic question applies: where does the figure originate, and who can account for it.
The Data Readiness Scan from csrdready.net is currently being built. Anyone who wants to help think through what a data point register should look like for an organisation with many branches and a long supplier chain can sign up for the waiting list. Nothing is being sold at this time, only a place reserved for when the instrument becomes available.
Once the overview of data points, sources and owners starts to fall into place, a follow-up question arises: which part of collecting, checking and updating that data is manual work, and which part can be done with the support of AI. FTE TO AI offers a work scan for that purpose, which calculates per task what portion of the work can be taken over, an addition that only becomes meaningful once it is clear exactly which tasks exist and where they come from.
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.