Connecting utility data to informed operational decisions

A business-led approach to elevating data quality, supporting operational analytics, and measuring results

Utility leaders rely on dependable data to make decisions about asset maintenance, resource allocation, regulatory compliance, and more. When data from different systems is fragmented or inconsistently defined, teams must spend valuable time combining and validating information before they can apply it to operational decisions.

In our white paper Evidence-based utility decisions: Where data platform value begins, Alex Johnson and Tom Quade explore how business and IT leaders can align their data platform strategy with operational priorities. Learn how effective governance and a shared data foundation support reliable analytics, how operational requirements shape adoption, and how leaders can measure the value of their data investments.

See how Logic20/20 helped a large California utility integrate 15 previously siloed data sets, providing analysts with regularly updated, quality-checked information for regulatory reporting and business analysis.

Key takeaways

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A glimpse into the white paper

From analytical development to dependable operational use

A shared data foundation provides dependable information for analysis. Models developed on local machines using exported files, however, may still require changes to data access, validation, and support arrangements before deployment.

Connecting experimentation with production helps reduce that rework. Think of analytical development as a “laboratory” for testing ideas and production as a “factory” delivering dependable outputs. Analysts can develop and test using shared, governed data, consistent validation, and a defined deployment path, with operational requirements considered from the start.

Consider an analyst developing an asset maintenance model. Using validated data sources that remain available in production reduces rebuilding before deployment. But the planner receiving its recommendations may also need current work orders, approval status, and crew availability to schedule the work. Considering those needs during development helps connect the analytical result to an action the team can take.

Clear responsibilities support dependable operation

• Business owners validate analytical outputs against operational requirements, assess user adoption, and evaluate their contribution to business decisions.
• Analysts and data scientists test model performance, document limitations, and evaluate changes.
• Data engineering and platform teams deploy analytical solutions, monitor data flows and service operation, and resolve technical issues.

Connect data investments to business priorities

Download the white paper to explore how utilities can establish dependable data foundations, integrate analytics into daily operations, and measure the business value of their data investments.