Data Analytics for Energy: Grid, Customer, and Regulatory Analytics

A utility builds 50 dashboards over two years across grid operations, customer service, finance, and regulatory. At the next leadership review, the VP of Distribution asks the same question she has asked every quarter: which feeders should be in the next capital plan for reliability improvement. The analytics team can show her SAIDI and SAIFI by feeder, can trend outage minutes, and cannot tell her the answer — because the answer requires joining outage history with asset age, vegetation exposure, load growth, DER penetration, and the consequence weighting that distinguishes a feeder serving a hospital from a feeder serving a field. Each of those datasets lives in a different system (OMS, asset registry, GIS, AMI, interconnection database). Nobody joined them. The dashboards report what happened. The VP needs analytics that tell her what to do.

Utility analytics that changes capital decisions requires the joins nobody makes. SCADA and AMI data at the feeder and transformer level. OMS outage history with cause codes. GIS asset data with age, condition, and vegetation exposure. Load and DER data from the planning models. Customer consequence weighting. With these joined at the geographic and asset level, the VP's question gets a real answer: these 15 feeders have the worst combination of reliability history, asset age, load growth, and customer consequence — invest here first. Without the join, analytics produces SAIDI trends nobody acts on.