Time to Evidence (h)
governance
systemic
This metric measures the time required to provide audit-ready evidence after a request, expressed in hours. It captures how quickly an organization can respond to audit, compliance, or reporting inquiries with complete, verifiable, and traceable documentation.
In a Green Agile context, Time to Evidence reflects process maturity and audit readiness. Long response times often indicate fragmented documentation, unclear ownership, or manual data collection. Short response times signal well-integrated systems, clear responsibilities, and established evidence pipelines.
Classification
- Category: Governance, Reporting & Compliance
- Measurement Frequency: per request
- Responsibility:
- Green Agile Coach: process ownership
- Engineering Team: evidence delivery
Impact
Reducing Time to Evidence lowers audit stress, minimizes last-minute manual effort, and increases confidence in sustainability reporting. It enables organizations to respond reliably to internal reviews, external audits, and regulatory inquiries.
Because this metric reflects coordination across tools, teams, and processes, it represents a systemic impact. Improving it typically requires changes to documentation practices, automation, data availability, and governance structures rather than isolated technical optimizations.
Calculation
Time to Evidence is calculated as the difference between the time an evidence request is made and the time complete evidence is delivered:
\[\text{Time to Evidence (h)} = t(\text{Evidence delivered}) - t(\text{Request})\]The desired direction is downward, with a common target of less than 24 hours, depending on audit scope and regulatory requirements.
Example
Assume an auditor requests evidence for an ESRS disclosure at Monday 10:00. The responsible team provides complete, audit-ready documentation at Tuesday 02:00.
The Time to Evidence is:
\[16 \text{ hours}\]This result indicates good audit readiness. If future requests consistently exceed the target, teams can investigate bottlenecks such as missing ownership, manual data aggregation, or unclear documentation standards.