Sustainability Data Readiness
Data gets manually consolidated once a year, while definitions differ per unit. That creates false precision at group level: tidy numbers resting on loose foundations. Ownership belongs to no one and everyone at once, and an audit trail is missing — painful the moment assurance comes into play. The pitfall is then buying a tool on top of an unorganized process: that only produces tidier reports on the same unreliable figures.
Which data points are actually needed, per material topic and standard, and which already exist where.
Per data point, the path from source through processing to its place in the report, with RACI and quality rules.
Requirements for tool selection derived from the designed process, plus a data maturity sub-score.
The front door delivers the Data Readiness Scan with the data point register as its backbone: what is needed and what already exists. Around it sits the source-to-report mapper, a fillable lineage tool following a fixed input-process-output structure, with RACI per data point and quality rules such as valid values and outlier signals. From the designed process follows a generator for functional requirements, so tool selection follows the process rather than the other way around. The three-day data bootcamp on a single topic — usually CO2 first — is a route 2/3 product delivered by the partner, but the design template also sits inside the tool so route 1 can run the same pattern independently.
Choose material topics, or start from esgia.
The register generator lists the required data points.
Record source, processing, ownership and quality rules per data point.
Sequence and lead-time indication per topic, followed by periodic re-measurement.
Every report ends in three routes. You choose; we deliver the analysis, not the engagement.
Route 1 — do it yourself: bootcamp template per topic, register export and controls checklist for assurance.
Route 2 — partly guided: the partner runs the bootcamps, the tool remains the register and the lineage.
Route 3 — outsourced: full setup by the partner, on FTE TO AI's own infrastructure.
The next step
Collecting data points is task work: retyping invoices, chasing emails, consolidating. The work scan calculates how much of that collection work AI can take over once the lineage is in place, pre-filled with the collection processes from the register.
Go to the work scan →We are building the datasets the analysis rests on. Let us know you're watching, and you'll hear from us as soon as the first measurements open up.
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.