Quality, Security & Reporting Readiness
Make data decision‑ready. You’ll apply practical quality controls (validation, reconciliations, sign‑offs), maintain a robust audit trail with versioned evidence, and protect privacy for HR‑linked metrics. The goal is full traceability of every KPI back to source for confident reporting and assurance (Batini & Scannapieco, 2016).
Quality Dimensions
- Accuracy – data reflects reality (e.g. meter readings match invoices and site logs).
- Completeness – no missing months, sites, fields or records for the KPI.
- Consistency – the same definitions, units and calculation methods are used across time and locations.
- Timeliness – data is captured, checked and approved in line with reporting deadlines.
- Validity – values fall within realistic ranges and pass basic input checks.
- Integrity – data is protected from unauthorised changes and all edits are logged.
These quality dimensions follow recognised information‑quality frameworks (Batini & Scannapieco, 2016).
Controls Toolkit
Input validation – mandatory fields, dropdown lists for units, and basic range checks at the point of entry.
Reconciliations (e.g. invoices vs meters) – compare two independent sources and investigate significant variances.
Spot checks – sample detailed records each month/quarter to verify evidence and calculations.
Sign‑offs – defined approvers review and confirm data before it is used for KPIs or external reports.
Exception thresholds – pre‑agreed variance limits that automatically trigger investigation and escalation.
Audit Trail & Documentation
Store original source files (invoices, meter exports, HR reports) in a clearly structured evidence folder.
Use version numbers for working files (v1, v2, v3) and archive older versions instead of overwriting.
Record who changed what, when and why in a simple change log or comments field.
Link each reported KPI to its underlying evidence so an auditor can re‑trace and reproduce the calculation.
This structured audit trail supports robust metadata and documentation practices (International Organization for Standardization, 2014; Batini & Scannapieco, 2016).
Security & Privacy
- Role‑based access – restrict who can view, edit or approve each dataset in the ESG system.
- Anonymise HR‑linked data – replace names with IDs and avoid unnecessary free‑text personal details.
- Retention schedules – define how long to keep raw data, reports and logs, then delete or archive securely.
- Document a simple data‑access request process so staff know how to obtain and share information safely.
These controls align with good practice in data‑quality and metadata standards (Batini & Scannapieco, 2016; International Organization for Standardization, 2014).
Reporting Readiness
- Each KPI has a clear definition, calculation method and named data owner.
- For every reported figure, you can point to the original source file(s) and intermediate calculations.
- All assumptions (e.g. emission factors, estimation methods) are documented, dated and centrally stored.
- A short “data story” explains how the KPI was produced so an external reviewer could reproduce it step by step.
