Enterprise Data 
Analytics Platform 
and AMI Operations
  • Case Study

Enterprise Data 
Analytics Platform 
and AMI Operations

Con Edison rolls out data analytics platform with C3.ai to improve operations across region

Project Challenge

In order to improve customer service and operations across its region, one of the largest integrated utilities in the United States has rolled out the C3 AI Suite and C3 AMI Operations application on AWS. Con Edison’s project objectives were to deliver on the utility’s commitments for presenting customer data, establish AMI operations across 5 million smart meters to ensure operational health, and build a federated data image platform for analytic capabilities.



Identified annual customer benefit


Deployment issues identified in 4 months


External systems accessing unified data

Project Highlights


Month Project


Source Systems


Customer Accounts


Annual Rows of Data Integrated

Smart Grid Analytics for Operational Health

In tandem with its 6 year-long smart meter rollout plan, Con Edison sought to implement Advanced Metering Infrastructure (AMI) operations on top of a comprehensive enterprise data analytics platform for improved operational insight and customer service for its base of more than four million customers.

The utility’s smart meter deployment will generate between 100 terabytes and 1 petabyte of data per year, so choosing a platform that could scale and continue to perform analytics on an ever-larger data set was vital. Not only can C3 AMI Operations support this scale, but it offers rapid time to value, a track record of successful projects, and the ability to expand to other applications over time.

To put the foundational enterprise data analytics platform in place, C3.ai and the utility worked together to aggregate two years of data from 13 source systems covering 5 million customer accounts. The joint team then designed requirements for third-part data use and configuration of the basic AMI Operations application. Once the integrated data image was in place, the team configured two machine learning algorithms and 50 analytics to identify deployment and installation issues and determine meter and network health.

The utility can now monitor smart meter deployment to identify any installation or configuration issues. The application also provides real-time status at any level of aggregation—from individual meter to the overall system—and a prioritized list of meters that require attention. In future phases, the company plans to build on its enterprise data analytics plan for additional customer insight applications and distribution and transmission automation capabilities.

Solution Architecture

Con Edison Platform Architecture

Project Timeline

Con Edison Project Timeline

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