Design and implementation of a scalable data visualisation system for eMobility charging networks
Herberholt, Fabian Ralf (2025)
Pro gradu -tutkielma
2025
School of Business and Management, Kauppatieteet
Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi-fe2025031417916
https://urn.fi/URN:NBN:fi-fe2025031417916
Tiivistelmä
The increasing adoption of electromobility demands reliable charging infrastructure for seamless operations and customer satisfaction. This thesis focuses on designing and implementing a scalable data visualisation system to enhance operational efficiency by automating data handling and providing near real-time insights into charger performance.
A mixed-method approach combined quantitative data processing with qualitative stakeholder feedback. For data preprocessing, transformation, and automation AWS applications such as Glue, Lambda, and QuickSight were used. Development followed a staged process, beginning with a mockup dashboard to gather customer requirements, followed by iterative improvements leading to final implementation.
Results show significant improvements in data accessibility, processing speed, and error detection. Pre-post implementation comparisons revealed an 86% reduction in processing time and a shift from two-day error detection delays to near real-time updates. Additionally, the data pipeline ensures scalability, supporting the company’s expansion while maintaining performance reliability.
Handling unstructured log files and ensuring usability across technical and non-technical stakeholders were key challenges. These findings highlight the importance of structured data pipelines and interactive visualisation tools in optimising eMobility operations.
A mixed-method approach combined quantitative data processing with qualitative stakeholder feedback. For data preprocessing, transformation, and automation AWS applications such as Glue, Lambda, and QuickSight were used. Development followed a staged process, beginning with a mockup dashboard to gather customer requirements, followed by iterative improvements leading to final implementation.
Results show significant improvements in data accessibility, processing speed, and error detection. Pre-post implementation comparisons revealed an 86% reduction in processing time and a shift from two-day error detection delays to near real-time updates. Additionally, the data pipeline ensures scalability, supporting the company’s expansion while maintaining performance reliability.
Handling unstructured log files and ensuring usability across technical and non-technical stakeholders were key challenges. These findings highlight the importance of structured data pipelines and interactive visualisation tools in optimising eMobility operations.