Seattle City Light is building its first enterprise data lake as the foundation for a multi-year Data Analytics and AI Enablement Program—offering a replicable model for peer utilities. This session presents SCL's end-to-end approach: a phased strategy beginning with foundational architecture and high-value reporting, then expanding into meter analytics and AI-enabled use cases. At its core is a model-driven, semantic approach to harmonizing disparate operational systems. Using the real example of integrating two very different asset and work management data models, SCL shows how a CIM-based semantic layer (SCLIM) resolves semantic mismatch, identity conflicts, and structural differences to produce trusted, cross-domain enterprise data.
 
The session emphasizes strategic and organizational decisions, not just technology—sharing what worked, what proved harder than expected, and how SCL measures success against business, regulatory, and operational outcomes. The goal: a practical blueprint other utility leaders can adapt.

Session Takeaways:

• A phased roadmap that balances near-term value with long-term AI ambitions
• How to scope and prioritize analytics use cases for measurable business outcomes
• Organizational and governance structures that sustain a data program
• How a CIM-based canonical model (SCLIM) harmonizes conflicting data
• Candid lessons learned that peer utilities can apply directly