Operational deficiencies such as inappropriate scheduling and malfunctioning components in HVAC systems attribute to substantial energy losses in commercial buildings and are often left unaddressed due to a lack of accessible analytical tools which can identify energy-saving measures. A novel multi-source, data-driven energy management toolkit was proposed, which synthesized established inverse energy modeling, anomaly detection and diagnostics, load disaggregation, and occupancy and occupant complaint analytics methods in the literature. The toolkit contains seven functions that input HVAC control, energy meter, Wi-Fi device count, and work order log data to detect hard and soft faults, improve sequences of operation, and monitor energy flows, occupancy patterns, and occupant satisfaction. These capabilities were demonstrated on a case study building and the generated insights identified a fault in operational logic which exacerbated heating energy use during afterhours.