Energinet Datahub

Optimising data science capabilities for organisational growth

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Energinet, a major energy company, partnered with Halfspace to address challenges in their data science operations and lay the groundwork for scaling advanced analytics across the organisation.

In Brief →

Challenge

Energinet faced multiple challenges in their data science operations. The company experienced high employee churn and job dissatisfaction in Team Enigma, their core data science team. There were also inefficiencies in innovation and development speed due to data scientists handling tasks outside their core competencies. Additionally, Energinet struggled with scaling data science capabilities across the organisation and was uncertain about the optimal structure for data science teams and supporting capabilities.

Solution

Halfspace developed a comprehensive solution based on four analytical tracks, centred around a tailored Data Science Value Chain framework. The first track focused on roles and responsibilities, providing clear definitions for various data-related roles and their tasks within the DS value chain. The second track addressed technical functionalities, offering a detailed analysis of necessary systems and functionalities for each step of the DS value chain. The third track concentrated on defined ownership, providing recommendations on organisational structure to support scaling of data science capabilities. Lastly, the fourth track covered general improvements, addressing standardisation, work methods, data literacy, and governance.

Approach

Halfspace introduced a Data Science Value Chain framework to simplify and consolidate Energinet's challenges. This framework, consisting of six steps (Data Identification, Data Integration, Development, Deployment, and Production), served as the foundation for three primary recommendation tracks. The approach ensured a consistent and easy-to-understand roadmap for Energinet's data science transformation.

Outcomes →

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Comprehensive handbook providing consistent and concise advice across multiple areas.

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Plan with clear instructions for parallel and prioritised workstreams.

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Enthusiastic adoption by Team Enigma's lead, who referred to the report as their "bible" for future operations.

Client

→ Energinet

✨

Name Nameson

Leader → Energinet

This report is our battle plan to apply the full power of data science, transforming Energinet into a data-driven powerhouse that will revolutionize the energy sector.

Dive deep →

Generative AI can impact the entire bid management process

Halfspace's solution addressed both the immediate pain points of Team Enigma and the long-term scalability needs of Energinet. By focusing on the entire data science value chain, the project provided a holistic view of necessary improvements, from role definitions to technical infrastructure.

The framework allowed Energinet to clearly define and optimise roles within data science teams, identify and implement necessary technical functionalities at each stage of the DS process, determine the most effective organisational structure for scaling data science capabilities, and address broader organisational factors affecting data science success, such as data literacy and governance.

Navigating the data science transformation journey

Good to keep in mind →

Early structured approach

Starting with a clear focus and analytical framework earlier in the project can help manage complexity and allow for deeper dives into specific areas.

Appropriate staffing

Ensure the project team includes the right mix of expertise, particularly when dealing with specialised technical areas like data science platforms.

Cross-functional collaboration

Success in data science transformation requires involvement from various stakeholders across the organisation, balancing central and business unit perspectives.