Statnett speeds up digitalization of their power network with Cognite Data Fusion®

Statnett, the system operator of the Norwegian power system, leverages Cognite Data Fusion®️ as the foundational data platform to scale data analytics and machine learning across their operations

$2 million in estimated annual savings

60% faster time for connection analysis

Data-driven maintenance planning

For users

Improved efficiency, faster delivery time, increased value

Statnett used Cognite Data Fusion® as the industrial data platform for its company-wide data management, enabling them to develop applications that give analysts easy access to contextualized data and reduce the time it takes to connect new assets to the grid

  • $1.2 million in estimated annual value
  • Less time spent collecting and cleaning data
  • 60% faster time for connection analysis

At the forefront of digital transformation

See Cognite Data Fusion® in action

Get in touch with our product experts to learn more and identify quick wins

We’re developing digital solutions and making them available to our core business, which will help us boost our efficiency, improve our customer response time, and reduce costs.

Beate Sander Krogstad

Executive Vice President for IT, Statnett

Through our partnership with Cognite we will be able to structure and use our data more efficiently and be able to use this to take the much-needed step towards increased efficiency and reduced costs for our customers.

Peer Olav Østli

EVP for IT, Statnett

This partnership strengthens our ambition to become more data-driven in our decision-making processes.

Beate Sander Krogstad

Executive Vice President for IT, Statnett

The Total Economic Impact of Cognite Data Fusion®

Customer interviews and financial analysis reveal an ROI of 400% and total benefits of $21.56M over three years for the Cognite Data Fusion® platform.

Summary of benefits

(three-year risk-adjusted)

Improved SME efficiency

$1.5M

Revenue gains arising from shorter shutdown period

$4.8M

Real-time data efficiencies

$2.3M

Optimized planned maintenance programme

$4.3M

Energy efficiency savings

$5.1M

Optimization of heavy machinery and industrial work-flow

$9M