From 360-Degree Data to Decision-Ready Utilities
Across our customer base, nearly every utility leader has heard a similar request: the business needs to understand why a high-value customer segment is seeing rising arrears, which assets should be prioritized for maintenance, where outreach could prevent avoidable calls, or how to identify meter exceptions before they delay billing. The request is not for another report. It is for an outcome: faster resolution, better prioritization, lower risk, and more confident action.
Yet, in our experience in many organizations, each new business question still starts with a hunt. Where is the data? Which field should be used? Why does one system call it a service point while another uses a different technical label? Which team has the trusted definition? Which extract is current? By the time the data is assembled, reconciled, and explained, the original decision moment may have passed.
This is why we strongly believe a true 360-degree view must mean more than visibility. It must mean that the utility can move from data to decisions without rebuilding the connective tissue each time. The goal is not simply to see more, but to act with confidence: one version of truth, shared definitions, and the ability for teams to answer new questions in hours rather than weeks.
The Shifts: Systems-wide, Standardized meaning, Self-service analytics
In our experience with many utilities of different sizes, including IOU, munis, co-ops, providing water, wastewater, electric, and gas,the path begins with three practical shifts.
First, utilities need to connect data across the systems that shape the customer and operational experience. That includes customer information, meter data, work and asset records, grid insights, engagement history, and relevant back-office context. Traditional point-to-point integration can support individual processes, but analytics and AI require a broader foundation: data that is continuously prepared, refreshed, and available in a trusted environment.
Second, utilities need to standardize meaning. Operational systems are optimized for transactions, not for cross-domain analysis. A meter installation, a service point, a premise, an account, a work order, and an outage can carry different meanings depending on the source system and the team using it. A shared semantic model turns technical structures into business language, aligns KPIs, and reduces the recurring debate over which number is right.
Third, utilities need to serve insight where work happens. Self-service analytics matters because business teams need not have to wait for every question to become a custom data effort. But self-service is only part of the story. Increasingly, the highest-value insight will be embedded directly into customer service, field operations, billing, collections, program management, and asset planning workflows.
How It Works: Bringing It All Together
We have found firsthand that the strongest use cases appear when data domains stop behaving like separate islands. Consider asset maintenance. A transformer health score is useful on its own, but it becomes more powerful when paired with revenue exposure, customer criticality, outage history, and field work constraints. Two assets may both be in poor condition, but the decision about which repair to prioritize should reflect operational risk, customer impact, financial exposure, and crew availability together.
The same principle applies to customer outcomes. In our experience, a high-bill conversation is rarely only a billing issue. It may involve weather, usage patterns, appliance behavior, rate eligibility, payment history, energy burden, prior outreach, outage context, and program participation. A service representative who sees only the bill amount is left to investigate. A representative supported by a connected profile can move more quickly from explanation to resolution.
Making AI Work For You: Better Judgment at Scale
AI has made the ambition of real-time, proactive utility operations feel much closer. Models can detect anomalous meter reads, identify likely leaks or outages, summarize customer interactions, predict payment risk, recommend next-best actions, and surface relevant knowledge during a live conversation. But AI is only as useful as the data, definitions, and workflow context around it.
A model supported by fragmented data may produce fast answers that still require manual verification. A model supported by governed data and shared semantics can become part of the operating fabric. It can help decide which AMI exceptions deserve review, which reads are ready for billing, which customers may need affordability outreach, which field activities should be prioritized, and which knowledge article is relevant to the conversation unfolding right now.
For utilities, this distinction matters. We have found the value of AI is not simply automation. It is better judgment at scale. That means AI should be designed to reduce manual analysis, elevate the right risk, and initiate appropriate actions while leaving employees in control of decisions that require human review, empathy, or policy judgment.
A modern customer service interaction demonstrates how the pieces come together. Before the customer finishes explaining the issue, the representative can see a concise activity summary: recent contacts, bill changes, outage notifications, program eligibility, and prior engagement. As the call unfolds, real-time signals can bring the most relevant insight to the front, such as a high-usage pattern, a potential meter issue, or a discount opportunity.
In our opinion, the representative should not have to leave the screen, search across systems, or translate technical data into customer language. Billing, usage, cases, service requests, outage details, knowledge articles, and recommended actions should remain available in context. If a field visit is needed, the workflow can help initiate it. If the customer needs conservation guidance, the representative can share targeted recommendations. At the end of the call, assisted authoring can summarize the interaction and categorize the topics discussed.
This is not about replacing the representative. It is about giving the representative the same advantage that the organization wants for every employee: the right context, at the right moment, in a workflow designed around the job to be done.
Trusted Operational Data On Demand
We encourage utilities also need to think beyond the immediate analytics interface. Advanced modeling, enterprise data science, and cross-functional planning increasingly depend on AI-ready operational data. AMI data is a prime example. It is rich, high-volume, and valuable, but preparing it for analytics and machine learning can be complex and slow.
When AMI measurements are continuously prepared, standardized, and published into formats that enterprise analytics platforms can use, the utility can accelerate model development and reduce time spent on data preparation. That enables use cases such as anomaly detection, load forecasting, demand flexibility planning, electric vehicle insights, appliance disaggregation, leak detection, and targeted program design. The point is not to move data for its own sake. The point is to make trusted operational data available wherever it can create decision value.
Incremental Progress Creates Its Own Momentum
We have found the move toward a true 360-degree utility does not require a single massive transformation. The most practical path is incremental. Utilities can begin by connecting high-value domains, standardizing the definitions that cause the most friction, and delivering trusted insights that create immediate confidence. From there, they can expand into cross-domain decision use cases, embed insights into operational workflows, and introduce AI where the data foundation is strong enough to support it.
For utility executives, our guidance is straightforward: data strategy is now operating strategy. The organizations that will move fastest are not simply the ones with the largest AI roadmap or the most information at their fingertips. They will be the ones that make data trustworthy, meaning consistent, and insight actionable inside the workflows that matter most.
The Future Utility Defined by Its Capability & Outcomes
We regard a true 360-degree view as not a destination screen. It is a capability. It connects systems without forcing every question into a new data project. It turns technical data into business meaning. It helps employees serve customers, manage assets, validate meter data, plan programs, and respond to grid events with greater confidence. And it gives AI the governed foundation it needs to become useful, explainable, and operationally relevant.
In our view, the future utility will not be defined by how much data it collects, but rather by how quickly, consistently, and responsibly it can turn that data into outcomes.
About the Author
Jason Kriendler is a Senior Director of Product Management at Oracle Utilities. He leads the product management team responsible for Oracle Utilities Data Intelligence and the analytics and AI strategy for Oracle’s utility product lines, including Customer Cloud Service, Work and Asset Cloud Service, and Meter Data Management.
Jason has more than 26 years of experience in utility technology, with a focus on using data to solve utility business problems. His work has included integrating advanced metering data into meter data management systems, automating service-order processes, and applying analytics and AI to everyday utility operations.
Today, Jason focuses on practical uses of data and AI that help utilities make better decisions and improve how work gets done. He holds a Bachelor of Business Administration in Management Information Systems from Texas A&M University.
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