Build sustainability data an auditor can trace, re-perform and sign: controls, explainable anomaly rules, evidence and assurance readiness.
7 sections · 28 lessons · 5h 4m total length
Sooner or later, every sustainability report meets a reader who is not inside the company: an external assurance provider, a regulator, a bank credit analyst or an investor's stewardship team. Their first question is almost always the same: show me where this number came from. If the answer is a chain of e-mails, a spreadsheet tab called "final_v7" and a colleague who has since left, the number is worth very little, however carefully it was calculated.
The Sustainability, Made Auditable book that describes Corprise's Phi platform opens with a blunt diagnosis: sustainability reporting fails in the same few places everywhere. The numbers sit in spreadsheets, e-mails and utility bills. Nobody can say which emission factor produced a figure, or who approved it. The same fact is typed again for GRI, for the regulator, for the bank and for the investor, and the versions drift apart.
The errors that break sustainability data are rarely sophisticated. They are the kind of slips any finance team would recognise:
Each one is small. Together, they make a report impossible to defend. Good control means catching these slips before a number reaches a report, and being able to show later that someone did.
Four pressures have turned sustainability data from a communications exercise into a controlled process. Regulators increasingly require disclosure and, in some cases, independent verification. Investors use frameworks such as ISSB, TCFD and CDP and compare companies on the figures. Large customers ask suppliers for emissions data. And assurance providers now sign opinions on sustainability information — which means they need a trail they can test.
A manufacturing group reports Scope 2 emissions of 41,200 tCO2e. During assurance, the practitioner samples one site and finds that the electricity figure was entered in kWh but the meter reports MWh for two months of the year. The error is corrected, but the practitioner now has to extend testing to every site, because nothing in the process would have caught the slip. The engagement runs three weeks over budget. The lesson is not about the formula — it is about the absence of a control.
This course follows the data from the moment it is measured to the moment an assurance provider signs a conclusion. You will learn:
Throughout, we use Phi as a worked example of how these controls can be built into software. The principles apply whatever tool you use.
Sustainability figures are increasingly read by people outside the company — regulators, lenders, investors and independent assurance providers — and every one of them eventually asks the same question: where did this number come from? This advanced course shows how to answer it with a controlled data process rather than a scramble at year end.
You will follow ESG data from collection to a signed assurance conclusion: data quality dimensions and ownership, a draft–submitted–approved–locked lifecycle with maker-checker review, explainable anomaly rules with worked examples, evidence and calculation lineage, tamper-evident audit logs, and reports frozen with a SHA-256 hash. The course then explains limited and reasonable assurance and the main international standards (ISAE 3000 (Revised), ISAE 3410 and ISSA 5000) at a practical level.
Corprise's Phi platform (phi.corprise.ai) is used throughout as a worked example of how these controls can be built into software, but the principles apply to any reporting process.
Sustainability, governance and investor-relations faculty at Corprise.ai
Corprise Academy is the teaching team behind Phi and Corprise IR. Our courses are written from real reporting projects in Egypt, the Gulf, North Africa and Europe.
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Corprise Academy
Corprise Academy
ESG Data Quality, Audit Trail & Assurance
$ 199.00
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