Operational Digital Twin and AI initiatives in wastewater treatment are often constrained by implicit data assumptions that prevent auditable optimisation decisions. We describe an evidence-governed workflow in which contract-aligned signals are declared and QA/QC gates are enforced before KPI computation or optimisation is attempted. Over a representative 180-day operational window, a master daily dataset is assembled from SCADA exports (flow, actuator runtimes and setpoints) and online water-quality sensors. Data acceptance applies engineering-range screening and MAD-based spike filtering using a five-hour window; downstream analytics proceed only when availability meets a configurable seventy-five per cent gate, with flow used as a hydraulic witness variable for cross-stream consistency. High frequency hood off-gas telemetry is filtered and aggregated to one-minute resolution prior to daily alignment, stabilising energy-relevant patterns while preserving traceability. The resulting information substrate enables interpretable KPI integration and supports action-to-cost models that map actuator setpoints to energy use, expressed in kilowatt-hours, and operational expenditure, expressed as monetary cost, supporting cost–energy optimisation under real-world coverage constraints.