Case Study: National Grid achieves cost savings and improved reliability with Anaconda Enterprise

How National Grid is leveraging the Anaconda Enterprise data science platform to reduce costs and increase reliability of energy delivery

National Grid, a major UK and US electricity and gas utility, needed to move from manufacturer-driven, time-based maintenance to a risk-based, data-driven approach for its transmission assets. The ETO analytics team required an enterprise-ready Python platform that met strict IT governance, secure package management, and 24/7 support, so National Grid selected Anaconda Enterprise from Anaconda to enable controlled, enterprise use of open-source Python tools.

Anaconda Enterprise delivered secure package governance, an on‑premises mirror, and access to libraries like scikit-learn and OpenCV, letting National Grid build reproducible machine-learning models, deploy APIs and visualizations, and share conda environments for rapid replication. The solution helped National Grid implement a more cost-effective, risk-based maintenance framework, improve modeling accuracy, and save engineering time—for example, using OpenCV to automatically extract seconds of interest from helicopter video and eliminate hours of manual review—and the team now relies on Anaconda Enterprise in daily workflows.

National Grid

Will Collins

Analytics Development Leader