San Diego Gas & Electric
Enterprise data platform modernization enabled real-time grid analytics and regulatory reporting
San Diego Gas & Electric needed to modernize its data infrastructure to support real-time grid analytics, regulatory reporting automation, and the data foundation for future AI initiatives. We designed and implemented a modern data platform that unified disparate data sources and reduced reporting cycles from weeks to hours.
The Challenge
SDG&E's data landscape had evolved organically over two decades. Critical operational data was spread across SCADA systems, GIS platforms, customer information systems, work management databases, and dozens of departmental spreadsheets and Access databases. Regulatory reporting — a significant operational burden for any utility — required analysts to manually extract data from multiple systems, reconcile discrepancies, and compile reports in a process that took weeks and was prone to errors. The utility's leadership recognized that grid modernization, renewable integration, and future AI initiatives all depended on a modern data foundation that did not exist.
Our Approach
We conducted a comprehensive data landscape assessment, cataloging 68 distinct data sources across the organization. We classified each by business criticality, data quality, and integration complexity, then designed a target-state data architecture built on a cloud data lakehouse model.
The architecture centered on three layers: an ingestion layer using a combination of CDC (for real-time operational data from SCADA and CIS) and batch ETL (for legacy systems), a transformation layer using dbt for version-controlled, tested data models, and a serving layer that fed both interactive dashboards and automated regulatory reports.
We implemented the platform in phases. Phase one focused on the highest-pain point — regulatory reporting — building automated pipelines that extracted, transformed, and validated the data required for CPUC filings. Phase two expanded to grid operations analytics, providing near real-time visibility into grid performance, outage patterns, and renewable generation. Phase three built the customer analytics capability, enabling segmented analysis of usage patterns, program enrollment, and service quality.
Throughout, we established a data governance framework with clear data ownership, quality monitoring, and access controls appropriate for a regulated utility.
The Results
Regulatory reporting cycle reduced from 3 weeks to 2 days — automated pipelines handle extraction, transformation, validation, and formatting, with analysts reviewing outputs rather than building reports manually.
Real-time grid analytics — operations teams now have dashboard visibility into grid performance with data latency under 5 minutes, enabling faster response to anomalies and outages.
68 data sources unified — a single platform provides consistent, governed access to data that was previously siloed across dozens of systems.
Data quality improved 40% — automated quality checks catch issues at ingestion rather than during report compilation, preventing errors from propagating.
Foundation for AI — the modern data platform provides the clean, accessible, governed data foundation that SDG&E's future AI initiatives will require.
$1.8M annual savings — from eliminated manual reporting effort, retired legacy reporting tools, and reduced data reconciliation overhead.
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