3-minute read
A large West Coast utility had invested in advanced wildfire risk modeling to support safer, more informed operational decisions. As adoption grew and demand for the underlying data and analytics increased, the organization saw an opportunity to build on that investment with a shared data foundation supporting operational, regulatory, and planning needs.
Logic20/20 helped develop a governed wildfire data mart that integrates asset and geographic information system (GIS) data with weather forecasts and observations, vegetation and customer data, and fire-spread forecasts to create a consistent, analytics-ready foundation. Teams now draw from tested, governed data and established relationships across multiple use cases rather than recreating foundational data work for individual applications and analyses. The data foundation now supports an expanding ecosystem of operational decision support, regulatory reporting, and capital planning capabilities. Across the broader wildfire analytics environment, one model’s run time decreased from seven days to four hours.
We brought our expertise in
- Utility operations and asset health
- Wildfire risk analytics
- Data engineering
- Data governance and quality
- Cloud data architecture
- Data product development
- Machine learning operations (MLOps)
Powering Californians with safe, reliable energy
Serving more than 3 million people across 4,000+ square miles, our client operates a complex energy grid spanning dense population centers, rural communities, and challenging terrain. The organization invests in grid resilience, wildfire mitigation, and technologies that help protect the communities it serves while balancing safety, reliability, and responsible financial stewardship.
Increasing demands on wildfire data
What began as an idea for applying advanced modeling to wildfire risk evolved into a scalable implementation that Logic20/20 helped bring into broader use across the organization. As adoption expanded, teams found new applications for the underlying data across a wider range of business functions. Each new application brought additional data preparation, integration, and maintenance needs, increasing the complexity of keeping information consistent across a growing analytics ecosystem.
Teams had access to raw geographic information system (GIS), asset, vegetation, customer, and weather data, but individual use cases often required independent data preparation and interpretation. Similar analyses could rely on different asset definitions or business logic, creating additional reconciliation work as the number of use cases increased.
As demand continued to grow, the utility asked Logic20/20 to expand the role of its wildfire data across a broader range of business needs. Specifically, the organization wanted to:
- Apply wildfire data across both analytics and application use cases
- Reduce repeated data preparation and reconciliation as new use cases emerged
- Support continued growth without compounding data complexity
Connected data supports scalable AI
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Building an end-to-end wildfire data service
Logic20/20 developed a governed data mart that connects asset and geographic information system (GIS) data with vegetation, customer, weather, and fire-spread data. The team created curated data models that standardize key definitions and relationships, giving analysts and applications consistent, analytics-ready data for wildfire risk analysis.
The resulting data mart supports multiple decision experiences, including:
- Wildfire operations
- Regulatory reporting and data requests
- Risk-informed planning
- Capital planning
With governed data and established relationships available across use cases, teams can apply common data models and business logic to new analytics and applications rather than recreating them independently.
Scaling on a foundation of trusted data
With a governed data foundation in place, the utility can apply trusted wildfire data across a growing range of operational, regulatory, and planning needs. Consistent definitions, shared data models, and established development practices help the organization manage complexity as adoption grows, strengthening confidence in the information used to support critical decisions.
The impact is evident in both adoption and efficiency. The number of data users grew from a handful to more than 120, while one model run that previously took seven days now takes four hours—a 98 percent reduction in elapsed time. Teams spend less time waiting for data and more time analyzing results and responding to business needs.
The utility now benefits from:
- Consistent data and definitions across wildfire analytics use cases
- More timely access to information for analysis and decision-making
- Reduced reconciliation effort when related analyses draw on common data
- Less reliance on siloed knowledge when maintaining and evolving wildfire data products
- A sustainable foundation for expanding analytics and applications without compounding data complexity
Managing complexity as wildfire data needs grow?
A governed data foundation can help you expand analytics and applications while maintaining consistency, quality, and trust across use cases.
Alexander Johnson is a Senior Solutions Architect at Logic20/20 with more than a decade of experience across data science, data engineering, and cloud platforms. He works with utility leaders to build strong data foundations and applied analytics that support risk-informed decisions across planning, operations, and response. His focus spans risk modeling, operational analytics, and modern data platforms, with an emphasis on practical, scalable approaches that improve clarity, reduce duplication, and strengthen decision confidence across utility risk domains.