CASE STUDY
DATA THEN FUELS THESE PROCESSES NOT ONLY BY ENABLING FUNCTIONALITY BUT ALSO BY ESTABLISHING SHARED DEFINITIONS IN OTHER WORDS A COMMON LANGUAGE ACROSS TEAMS.
By building a comprehensive data model and centralized data lakehouse, MET ensures relevant risk and performance data is available to analysts and leadership via standardized reports. This supports informed decision-making from subsidiary sales teams up to the Group CEO. The transformation also emphasizes automating data collection processes to handle large volumes efficiently, reducing manual workload and enhancing scalability.
You used to work at Swarovski, a company with a very different business model. How did your experience with predictive intelligence in a global retail environment shape your peoplecentered approach to Digital Transformation in the energy sector?
My experience at Swarovski, a highly customer-centric retail company, at least partially shaped my approach to Digital Transformation at MET by highlighting the importance of personalized, data-driven customer engagement.
By mapping the entire IT landscape and tagging sensitive data, MET demonstrates control and readiness for regulatory scrutiny. This governance framework helps manage risks related to data misuse and algorithmic bias.
Beyond the technology itself, what have been the most significant challenges in fostering a culture of digital change within a traditional energy company and what strategies have you found most effective?
The biggest challenge is cultural, shifting from decisions based on relationships and habits to fact-based, data-driven decision-making. This requires not only making data accessible but also changing deep-rooted habits, which demands stronger change management than just bringing a new software.
Communication and training are key enablers informing teams about process changes and providing training to build skills in process definition, project management and data use. Collaborating closely with HR or Internal Communications are essential to decentralize knowledge and embed these practices.
Looking ahead, what do you see as the next major digital frontier for the energy industry and what is MET Group doing today to prepare for it?
The next major digital frontier in the energy industry is with integrating advanced AI, especially generative and agentic AI, to transform processes. While MET Group currently uses AI in limited ways, there’ s significant potential to expand by leveraging enhanced data transparency and applying sophisticated machine learning models.
However, leveraging Gen / agentic AI will require fundamentally redesigning processes and ways of working to enable partial automation alongside human input. Preparing for this shift involves continuing to build strong data foundations, ensuring process transparency and fostering rational decision-making to fully embrace AI’ s capabilities. To get there, we are strengthening our data and process foundations today. p
At Swarovski, we used data to predict which necklace someone might buy. At MET, we use it to manage million-euro energy risks. Very different – but the principle is the same: know your customer.
How does your digital strategy, particularly in data governance, address challenges such as regulatory complexity to ensure MET Group’ s operations remain compliant?
MET Group’ s digital strategy prioritizes data governance to secure data quality which supports smooth and safe operation as well as reliable information. But it is also a key enabler to ensure transparency and compliance with complex regulations such as GDPR and the upcoming EU AI Act.
16 INTELLIGENTCIO EUROPE www. intelligentcio. com