OMV powers its digital transformation with Cognite Data Fusion®
OMV, the international, integrated oil, gas, and chemicals company, leverages Cognite Data Fusion®️ as the foundational data platform to accelerate its digital transformation and drive operational improvements that make the organization more effective and unlock data-driven business opportunities along the entire value chain
50% reduction in inspection planning time
6% deferment savings on production
Extends the life of gas fields by 35 days per year
Making data available for decision-making
OMV used Cognite Data Fusion®, to combine data from six different systems, giving production engineers a simple, efficient way of monitoring well operating envelopes:
- 5 hours saved per week
- Reduced production losses
- Automated workflows and increased productivity of their domain experts
At the forefront of digital transformation
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In their own words
Cognite Data Fusion® is an integrated part of our enterprise architecture and is the best platform to consolidate and liberate data for our surface data domains. We have built several use cases and applications on this liberated data which can now be easily scaled to our physical assets.
Markus Berghofer
SVP for IT and Digital, OMV Energy
The future of the oil & gas industry is digital. OMV is committed to leading the change as a digital frontrunner together with Aker BP and Cognite
Johann Pleininger
Executive Board member responsible for Upstream and Deputy Chairman, OMV Energy
The successful integration of Cognite Data Fusion® into our landscape is key to liberating data for further accelerating our digital application ecosystem
Markus Berghofer
SVP for IT and Digital, OMV Energy
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