From smart meter data to smarter grid operations

Every day, utilities collect large volumes of data from advanced metering infrastructure (AMI), capturing detailed information on consumption patterns, voltage performance and asset behavior across the grid. This data has the potential to improve reliability, strengthen operations and enable more proactive decision-making. Yet much of this value remains untapped, constrained by fragmented systems and limited accessibility at scale.

For a leading U.S. utility, this gap represented both a challenge and a missed opportunity. Despite significant investments in smart meter technology, data remained slow to process, difficult to access and heavily dependent on technical teams for analysis. Insights into emerging issues, load patterns and asset performance were often delayed, limiting the organization’s ability to respond proactively to changing grid conditions.

We partnered with the utility to transform how AMI data is managed, accessed and used across the business. Rather than simply improving data availability, the approach focused on changing how teams interact with data. By implementing a modern data and AI platform on Databricks and introducing conversational analytics through Databricks Genie, operational users can now engage directly with data, explore trends in real time and investigate the drivers behind grid performance without relying on complex queries or technical support.

From fragmented data to operational insight at scale

The core challenge in AMI analytics is not data availability, but usability at scale. While utilities generate large volumes of smart meter data, extracting timely and actionable insights remains difficult.

For this organization, key challenges included fragmented data across systems, slow processing of high-volume datasets, reliance on technical resources and data quality issues that reduced confidence in reporting. As a result, teams spent more time preparing data than acting on it, delaying the detection of anomalies and limiting the ability to anticipate and address emerging issues.

Built on the Databricks Lakehouse Platform, the solution addresses these constraints by unifying analytics across platforms, processing data at scale and applying consistent governance. This creates a trusted foundation for analytics. Instead of working with disconnected datasets and manual processes, teams can now access timely, reliable insights that support faster decision-making.

Access to insight is no longer limited to technical users. With natural-language access to curated data through Databricks Genie, operational teams can explore patterns, validate assumptions and identify emerging issues as they occur.

Building the solution on Databricks

Turning high-volume AMI data into usable insights requires both scalable data engineering and intuitive access. While many organizations can store and process data, far fewer make it directly usable for the teams responsible for grid operations.

In this case, the objective was to establish a unified, governed data environment while enabling direct interaction with that data through a conversational interface.

Key elements of the solution include:

  • A scalable data foundation that processes high-volume AMI data using Spark-based pipelines
  • Centralized governance, lineage and access management through Unity Catalog
  • Automated data quality checks to improve trust in analytics
  • Machine learning models for forecasting, anomaly detection and classification, managed through MLflow
  • Management-ready dashboards combined with conversational analytics
  • Natural-language access through Databricks AI/BI Genie, enabling users to query data in plain English

Because every layer runs on a single platform, the solution is governed end-to-end. Data remains secure, traceable and adaptable as business needs evolve. The result is a system where insights are not only generated but immediately accessible to the teams responsible for grid operations.

Enabling more proactive, data-driven grid operations.

For the utility, the impact extends beyond improved data access. The ability to work with AMI data more effectively enables faster, more informed decision-making across the organization.

  • Grid operations teams gain real-time visibility into meter performance, transformer loads and power quality, enabling earlier detection of anomalies and more proactive responses.
  • Outage management teams benefit from continuous data feeds and geolocation insights that support faster identification of outages and more efficient restoration efforts.
  • Maintenance teams can identify potential issues earlier through automated anomaly detection, enabling a shift toward predictive maintenance.
  • Planning and forecasting teams leverage predictive models that incorporate weather, location and historical usage data to improve load forecasting and grid management.
  • Business users across the organization can access and explore data directly, reducing reliance on specialized technical resources.

The result is a shift from reactive operations to a more proactive, data-driven approach to managing grid performance and reliability.

A pattern that extends beyond a single use case

While this transformation focused on AMI data, the underlying challenge is common across industries. Organizations continue to generate increasing volumes of data but often struggle to make that data accessible and actionable.

By combining a unified data platform with strong governance and intuitive, conversational access, organizations can move beyond static reporting and embed analytics directly into day-to-day decision-making.

This reflects a broader shift in how data and AI are applied, not just as tools for analysis, but as capabilities integrated into operational workflows across the enterprise.

Operationalizing data for grid reliability and performance

As utilities continue to modernize the grid, the ability to operationalize data becomes central to improving reliability and performance. AMI data has always held significant potential.

With CGI and Databricks, utilities can now use that data to support faster decisions, enable proactive operations and strengthen grid resilience.