Innovative Offshore Wind Developer
A leading global offshore wind developer with a diverse portfolio across Europe, Asia Pacific, and the Americas.
The challenge.
Centralising Resource Management Across Disconnected Systems
The client required a solution to centralise resource management data from multiple disconnected systems; timesheet information originated from complex Excel workbooks and external platforms that exported data manually into spreadsheets. This fragmented data needed to be merged; validated; and enriched with calculations such as resource allocations before being pushed to a centralised resource management system.
The solution.
1
Unified Data Storage with Lakehouses
Microsoft Fabric Lakehouses were used to store raw and transformed data across multiple stages. This provided a centralised, scalable foundation for managing diverse inputs and enabled consistent data handling throughout the resource management process.
2
Advanced Processing with Spark Notebooks
Spark notebooks were used to apply complex logic including validation, transformation, and resource allocation calculations. They also handled API interactions with the target system, enabling seamless data exchange and pre-publication adjustments.
3
Automation and Reporting with Data Pipelines and Power BI
Data pipelines orchestrated the end-to-end process and triggered alerts as needed. Power BI applied dynamic calculations and presented outputs via Power BI reports and Excel workbooks connected to the semantic model, ensuring flexible access and meeting all reporting requirements.
The results.
What did we achieve in 6 months?
User Training and Adoption
Users were successfully trained on the new ways of working, enabling them to confidently manage and adjust resource data using Microsoft Fabric tools. This helped embed the solution into daily operations and ensured long-term sustainability.
Significant Time Savings
The new solution reduced manual effort by over 90%, streamlining previously labour-intensive processes. Teams could now focus on analysis and decision-making rather than repetitive data handling, dramatically improving operational efficiency.
Error and Cost Reduction
By eliminating manual data manipulation and introducing validation logic, the solution reduced errors and associated costs. This improved data quality and reduced the risk of misreporting or misallocation of resources.
Faster, More Timely Reporting
Reporting became significantly more timely, as the effort required to produce outputs was drastically reduced. Teams could generate insights quickly, supporting better planning and more responsive decision-making.
